US 20010032029 A1 Abstract The present inventionrelates generally to a system and method for operations management. More specifically, the present invention determines robust processes for performing one or more tasks.
Claims(21) 1. A method for designing an infrastructure to perform one or more tasks in an environment of resources comprising the steps of:
determining one or more relations among the resources; constructing a graph representation of said relations and the resources; determining one or more paths through said graph representation wherein each of said paths represents a process for performing at least one of the tasks; and determining at least one group of those of said resources that lie along said one or more paths, said at least one group having a minimal risk. 2. A method for designing an infrastructure as in claim 1 3. A method for designing an infrastructure as in claim 1 4. A method for designing an infrastructure as in claim 1 5. A method for designing an infrastructure as in claim 1 6. A method for designing an infrastructure as in claim 1 determining a plurality of anti-correlated families wherein each of said anti-correlated families contains two or more of said resources lying along said one or more paths that are anti-correlated; and
determining one or more perspective groups of said resources from said plurality of anti-correlated families.
7. A method for designing an infrastructure as in claim 6 receiving one or more time series corresponding to one or more of said groups of resources.
8. A method for designing an infrastructure as in claim 7 determining correlations among said resources.
9. A method for designing an infrastructure as in claim 8 selecting at least one of said resources to be a first member of one of said anti-correlated families;
determining one or more of said resources having a value of said correlation with said first member of said anti-correlated family that is less than a negative limit; and
including said one or more resources having a correlation value that is less than the limit as additional members in said anti-correlated family.
10. A method for designing an infrastructure as in claim 9 claim 9 11. A method for designing an infrastructure as in claim 10 eliminating drift from said one or more time series to create corresponding one or more detrended time series.
12. A method for designing an infrastructure as in claim 11 13. A method for designing an infrastructure as in claim 12 14. A method for designing an infrastructure as in claim 13 determining percentages of said resources from said selected family to include in one of said perspective groups.
15. A method for designing an infrastructure as in claim 14 16. A method for designing an infrastructure as in claim 15 17. A method for designing an infrastructure as in claim 16 18. Computer executable software code stored on a computer readable medium, the code for designing an infrastructure to perform one or more tasks in an environment of resources, the code comprising:
code to determine one or more relations among the resources; code to construct a graph representation of said relations and the resources; code to determine one or more paths through said graph representation wherein each of said paths represents a process for performing at least one of the tasks; and code to determine at least one group of those of said resources that lie along said one or more paths, said at least one group having a minimal risk. 19. Computer executable software code stored on a computer readable medium, the code for designing an infrastructure to perform one or more tasks in an environment of resources as in claim 18 code to determine a plurality of anti-correlated families wherein each of said anti-correlated families contains two or more of said resources lying along said one or more paths that are anti-correlated; and code to determine one or more perspective groups of said resources from said plurality of anti-correlated families. 20. A programmed computer system for designing an infrastructure to perform one or more tasks in an environment of resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes
code to determine one or more relations among the resources; code to construct a graph representation of said relations and the resources; code to determine one or more paths through said graph representation wherein each of said paths represents a process for performing at least one of the tasks; and code to determine at least one group of those of said resources that lie along said one or more paths, said at least one group having a minimal risk. 21. A programmed computer system for designing an infrastructure to perform one or more tasks in an environment of resources comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory as in claim 20 code to determine a plurality of anti-correlated families wherein each of said anti-correlated families contains two or more of said resources lying along said one or more paths that are anti-correlated; and code to determine one or more perspective groups of said resources from said plurality of anti-correlated families. Description [0001] The present invention relates generally to a system and method for operations management. More specifically, the present invention determines robust processes for performing one or more tasks. [0002] An environment includes entities and resources as well as the relations among them. An exemplary environment includes an economy. An economy includes economic agents, goods, and services as well as the relations among them. Economic agents such as firms can produce goods and services in an economy. Operations management includes all aspects of the production of goods and services including supply chain management, job shop scheduling, flow shop management, the design of organization structure, etc. [0003] Firms produce complex goods and services using a chain of activities which can generically be called a process. The activities within the process may be internal to a single firm or span many firms. A firm's supply chain management system strategically controls the supply of materials required by the processes from the supply of renewable resources through manufacture, assembly, and finally to the end customers. See generally, Operations Management, Slack et al., Pitman Publishing, London, 1995. (“Operations Management”). [0004] Other types of entities similarly perform service using processes. As a non-limiting example, military organizations perform logistics within a changing environment to achieve goals such as establishing a beachhead or taking control of a hill in a battlefield. [0005] The activities of the process may be internal to a single firm or span many firms. For those activities which span many firms, the firm's supply chain management system must perform a variety of tasks to control the supply of materials required by the activities within the process. For example, the supply chain management system must negotiate prices, set delivery dates, specify the required quantity of the materials, specify the required quality of the material, etc. [0006] Similarly, the activities of the process may be within one site of a firm or span many sites within a firm. For those activities which span many sites, the firm's supply chain management system must determine the number of sites, the location of the sites with respect to the spacial distribution of customers, and the assignment of activities to sites. This allocation problem is a generalization of the quadratic assignment problem (“QAP”). [0007] For the activities of the process within a site of a firm, the firm's job shop scheduling system assigns activities to machines. Specifically, in the job shop scheduling problem (“JSP”), each machine at the firm performs a set of jobs, each consisting of a certain ordered sequence of transformations from a defined set of transformations, so that there is at most one job running at any instance of time on any machine. The firm's job shop scheduling system attempts to minimize the total completion time called the makespan. [0008] Manufacturing Resource Planning (“MRP”) software systems track the number of parts in a database, monitor inventory levels, and automatically notify the firm when inventory levels run low. MRP software systems also forecast consumer demand. MRP software systems perform production floor scheduling in order to meet the forecasted consumer demand. [0009] Firms must also design an organization structure. The structure for an organization includes a management hierarchy and a distribution of decision making authority to the people within the organization. The structure of a firm effects the propagation of information throughout the firm. [0010] Previous research for supply chain management has studied the effects of demand on the production rate at earlier or upstream operations along the supply chain. Additional research has classified the different relationships which exist in supply chains. This research has classified supply chain relationships as: integrated hierarchy, semi-hierarchy, co-contracting, coordinated contracting, coordinated revenue links, long term trading commitments and short term trading commitments. See Operations Management, Chapter 14. [0011] Previous research for MRP has produced algorithms to compute material volume requirements and to compute timing requirements for those materials using Gantt charts. Other MRP algorithms such as the Optimized Production (OPT) schedule production systems to the pace dictated by the most heavily loaded resources which are identified as bottlenecks. See Operations Management, Chapter 14. [0012] Additional research has attempted to automate the exchange of goods and services among buyers and sellers. For example, U.S. Pat. No. 5,689,652 discloses a method for matching buy and sell orders of financial instruments such as equity securities, futures, derivatives, options, bonds and currencies based upon a satisfaction profile using a crossing network. The satisfaction profiles define the degree of satisfaction associated with trading a particular instrument at varying prices and quantities. The method for matching buy and sell orders inputs satisfaction profiles from buyers and sellers to a central processing location, computes a cross-product of the satisfaction profiles to produce a set of mutual satisfaction profiles, scores the mutual satisfaction profiles, and executes the trades having the highest scores. [0013] U.S. Pat. No. 5,136,501 discloses a matching system for trading financial instruments in which bids are automatically matched against offers for given trading instruments for automatically providing matching transactions in order to complete trades using a host computer. Likewise, U.S. Pat. No. 5,727,165 presents an improved matching system for trading instruments in which the occurrence of automatically confirmed trades is dependent on receipt of match acknowledgment messages by a host computer from all counter parties to the matching trade. [0014] However, previous research on operations management has not adequately accounted for the effect of failures or changes in the economic environment on the operation of the firm. For example, machines and sites could fail or supplies of material could be delayed or interrupted. Accordingly, the firm's supply chain management, job shop scheduling and organization structure must be robust and reliable to account for the effect of failures on the operation of the firm. [0015] Similarly, the economic environment changes with the introduction of new goods and services, new production technologies, new legislation and the extinction of older goods and services. Similarly, changes in the supply and demand for materials also effects the economic environment. For example, the contingent value to buyer and seller of goods or services, the cost of producing the next kilowatt of power for a power generating plant, and the value of the next kilowatt of power to a purchaser effect the economic environment. Accordingly, the firm's supply chain management, job shop scheduling and organization structure must be flexible and adaptive to account for the effect of changes to the firm's economic environment. [0016] Moreover, previous research for automating the exchange of financial instruments have disadvantages. Most important, these methods have a limited application as they do not apply to the general exchange of goods and services among economic agents. Instead, they are focused towards financial transactions. Next, the trades for each of these systems must be processed at a central computing location. Next, these systems do not have real-time support for trader preferences which vary with time. [0017] Accordingly, there exists a need for a system and method to determine reliable and adaptive processes for performing one or more tasks. [0018] The present invention presents a system and method to determine reliable and adaptive processes for performing one or more tasks. The present invention presents a framework of features which include technology graphs and risk management to achieve its reliability and adaptability. [0019] It is an aspect of the present invention to present a method for designing an infrastructure to perform one or more tasks in an environment of resources comprising the steps of: [0020] determining one or more relations among the resources; [0021] constructing a graph representation of said relations and the resources; [0022] determining one or more paths through said graph representation wherein each of said paths represents a process for performing at least one of the tasks; and [0023] determining at least one group of those of said resources that lie along said one or more paths, said at least one group having a minimal risk. [0024] It is a further aspect of the invention to present a method for designing an infrastructure wherein said determining at least one group having a minimal risk step comprises the steps of: [0025] determining a plurality of anti-correlated families wherein each of said anti-correlated families contains two or more of said resources lying along said one or more paths that are anti-correlated; and [0026] determining one or more perspective groups of said resources from said plurality of anti-correlated families. [0027]FIG. 1 provides a diagram showing a framework for the major components of the system and method for operations management. [0028]FIG. 2 displays a diagram showing a composite model of a firm's processes and organizational structure including the relation between the firm's processes and organizational structure. [0029]FIG. 3 shows an exemplary aggregation hierarchy [0030]FIG. 4 [0031]FIG. 4 [0032]FIG. 4 [0033]FIG. 5 shows an exemplary technology graph. [0034]FIG. 6 provides a dataflow diagram [0035]FIG. 7 provides a flow diagram [0036]FIG. 8 displays a flow diagram of an algorithm to perform landscape synthesis. [0037]FIG. 9 displays a flow diagram of an algorithm to determine the bases v. for landscapes. FIG. 10 shows the flow diagram of an overview of a first technique to identify a firm's regime. [0038]FIG. 11 shows the flow diagram of an algorithm [0039]FIG. 12 [0040]FIG. 12 [0041]FIG. 13 [0042]FIG. 13 [0043]FIG. 14 shows an illustration of the architecture of the system of the present invention. [0044]FIG. 15 provides a flow diagram describing a method executed by the resource providing nodes. [0045]FIG. 16 displays a flow diagram of a method for allocating resources using a market-based scheme which could also execute on a resource providing node. [0046]FIG. 17 provides a flow diagram for determining optimal values of parameters of methods performing resource allocation and load balancing. [0047]FIG. 18 provides a flow diagram of a method for evaluating and minimizing risk. [0048]FIG. 19 provides the results of the method for evaluating and minimizing risk from executing on 500,000 random draws from this “toy” world. [0049]FIG. 20 displays the histograms that determine the number of children to draw from during execution of the method for evaluating and minimizing risk. [0050]FIG. 21 displays a flowchart illustrating the method for portfolio modification. [0051]FIGS. 22, 23 [0052]FIG. 24 displays a picture illustrating the anti-correlation relationship among family members that was generated by a method to create a portfolio of a plurality of assets with minimal risk. [0053]FIG. 25 shows a flow diagram of an exemplary method [0054]FIG. 26 discloses a representative computer system [0055]FIG. 1 provides a diagram showing a framework for the major components of the system and method for operations management called United Sherpa [0056] Without limitation, many of the following embodiments of the invention, United Sherpa [0057] Modeling and Simulation [0058] The modeling component [0059] Without limitation, the following embodiments of the invention, United Sherpa [0060] As is known to persons of ordinary skill in the art, objects are distinguishable entities and have attributes and behavior. See Object Oriented Modeling and Design, Rumbaugh, J., Prentice hall, Inc. (1991), Chapter 1. Further, objects having the same attributes and behavior are grouped into a class. In other words, objects are instances of classes. Each class represents a type of decision making unit. The representation of real world entities with objects is described in co-pending U.S. patent application Ser. No. 09/080,040, System and Method for the Synthesis of an Economic Web and the Identification of New Market Niches, the contents of which are herein incorporated by reference. [0061] Decision making units in the organizational structure model [0062]FIG. 3 shows an exemplary aggregation hierarchy [0063] Moreover, OrgSim can model decision making units at varying degrees of abstraction. For example, OrgSim can represent decision making units as detailed as an individual employee with a particular amount of industrial and educational experience or as abstract asa standard operating procedure. Using this abstract modeling ability, OrgSim can represent a wide range of organizations. Next, OrgSim [0064] These modeling aspects enable OrgSim [0065] Through simulation, Orgsim [0066] OrgSim represents each good, service and economic entity associated with a firm's processes with an object in the process model [0067] OrgSim includes an interface to enable a user to define the decision making units, the structure of the communication network among the decision making units, the temporal aspect of the information flow among the decision making units, etc. Preferably, the user interface is a graphical user interface. [0068] Preferably, OrgSim provides support for multiple users, interactive modeling of organizational structure and processes, human representation of decision making units and key activities within a process. Specifically, people, instead of programmed objects, can act as decision making units. Support for these additional features conveys at least two important advantages. First, the OrgSim model [0069] Preferably, OrgSim also includes an interface to existing project management models such as Primavera and Microsoft Project and to existing process models such as iThink. [0070] Without limitation, the following embodiments of the Enterprise model [0071] The Enterprise model [0072]FIG. 4 [0073] The ParticipantSupport [0074] Preferably, affordance sets model the roles of resources and the contractual terms. An affordance is an enabler of an activity for a suitably equipped entity in a suitable context. A suitably equipped entity is an economic agent which requests a resource, adds value to the resource, and offers the resulting product into a supply chain. A suitable context is the “inner complements” of other affordances which comprise the resource. Affordances participate in other affordances. Further, an affordance can contain sets of other affordances which are specializations of the affordance. Preferably, the situated object web [0075] Affordances have associated values. For example, a value of an affordance specified by an RBConsumer object [0076] The ResourceBus [0077] Preferably, the ResourceBus [0078] For example, if a consumer object requests a complementary object representing a # [0079] The situated object web [0080] As RBProducer objects [0081]FIG. 4 [0082] Execution of the situated object web [0083] United Sherpa [0084] However, the object oriented approach of the present invention has advantages over MRP or other conventional business modeling tools because the object oriented approach provides a more direct representation of the goods, services, and economic agents which are involved in a firm's processes. Conventional business tools typically build numerical models which describe business operations exclusively in terms of numerical quantities. For example, conventional business tools have numerical models representing how the inventory of material resources vary with time. In contrast, the modeling component [0085] In contrast to numerical models, the object oriented approach of the present invention is also amenable to what if analysis. For example, the modeling component [0086] As indicated by the previous discussion of FIGS. [0087] OrgSim [0088] Technology Graph [0089]FIG. 5 shows an exemplary technology graph. A technology graph is a model of a firm's processes. More specifically, a technology graph is a multigraph representation of a firm's processes. As previously explained, a firm's processes produce complex goods and services. As is known to persons of ordinary skill in the art, a multigraph is a pair (V,E) where V is a set of vertices, E is a set of hyperedges, and E is a subset of P(V), the power set of V. See Graph Theory, Bela Bollobas, Springer-Verlag, New York, 1979, (“Graph Theory”) Chapter 1. The power set of V is the set of subsets of V. See Introduction to Discrete Structures, Preparata and Yeh, Addison-Wesley Publishing Company, Inc. (1973) (“Introduction to Discrete Structures”), pg 216. [0090] In the technology graph (V,E) of a firm's processes, each vertex v of the set of vertices V represents an object. More formally, there exists a one-to-one correspondence between the set of objects representing the goods, services, and economic agents and the set of vertices V in the technology graph (V,E) of the firm's processes. A function denoted by g: O->V from the set of objects [0091] In the technology graph (V,E) of a firm's processes, each hyperedge e of the set of hyperedges E represents a transformation as shown by FIG. 5. The outputs of the hyperedge e are defined as the intermediate goods and services [0092] Resources [0093] The objects and transformations among the objects in the technology graph H=(V, E) constitute a generative grammar. As is known by persons of ordinary skill in the art, context-free grammars represent transformations or productions on symbol strings. Each production specifies a substitute symbol string for a given symbol string. The technology graph H=(V, E) extends the principles of context-free grammars from symbol strings and transformations among symbol strings to objects and transformations among objects. The expressiveness of the technology graph H=(V, E) is higher than that of context-free grammars as hypergraphs can represent multidimensional relationships directly. The technology graph H=(V, E) also expresses a context sensitive grammar. [0094] Each transformation in the technology graph H=(V, E) may specify a substitute hypergraph for a given hypergraph. Accordingly if a subgraph within a hypergraph matches a given hypergraph in a transformation, the subgraph is removed and replace by the substitute hypergraph. The resulting hypergraph is derived from the original hypergraph. [0095]FIG. 6 provides a dataflow diagram [0096] In step [0097] In step [0098] In step [0099] In one embodiment, the method maintains all vertices created by execution of step [0100] In subsequent iterations of the loop of the flow graph of FIG. 6, step [0101] The set of transformations T can be held fixed throughout the execution of the technology graph synthesis method [0102] Technology Graph Applications [0103] The paths in the technology graph H=(V, E) which begin at vertices corresponding to objects in the founder set and end at vertices corresponding to finished goods represent the processes for producing the finished goods from the objects in the founder set. A path P [0104] The technology graph H=(V, E) also contains information defining a first robust constructability measure of a terminal object representing a finished good or service. The first robust constructability measure for a terminal object is defined as the number of processes or construction pathways ending at the terminal object. Process redundancy for a terminal object exists when the number of processes or construction pathways in a technology graph exceeds one. Failures such as an interruption in the supply of a renewable resource or the failure of a machine cause blocks along construction pathways. Greater numbers of processes or construction pathways to a terminal object indicate a greater probability that a failure causing blocks can be overcome by following an alternate construction pathway to avoid the blocks. Accordingly, higher values of the first robust constructability measure for a terminal object indicate higher levels of reliability for the processes which produce the finished good or service represented by the terminal object. Further, the technology graph extends the traditional notion of the makespan. [0105] The technology graph H=(V, E) also contains information defining a second robust constructability measure of a terminal object representing a finished good or service. The second robust constructability measure for a terminal object is defined as the rate at which the number of processes or construction pathways ending at the terminal object increases with the makespan of the process. For example, suppose a terminal object can be constructed with a makespan of N time steps with no process redundancy. Since there is no process redundancy, a block along the only construction pathway will prevent production of the terminal object until the cause of the block is corrected. The relaxation of the required makespan to N+M time steps will increase the number of construction pathways ending at the terminal object. Accordingly, failures causing blocks can be overcome by following an alternate construction pathway to the terminal object. In other words, while the minimum possible makespan increased by M time steps, the resulting greater numbers of processes or construction pathways to the terminal object led to greater reliability. Thus, the present invention extends the notion of a makespan to include the concept of robust constructability. [0106] The technology graph H=(V, E) contains additional robust constructability measures of a class or family of terminal objects representing different finished goods or services. As previously discussed, objects having common attributes and behavior are grouped into a class. See [0107] The additional robust constructability measures involve vertices which exist within the construction pathways of two or more terminal objects. These objects represented by these vertices are called poly-functional intermediate objects because two or more terminal objects can be constructed from them. For example, consider two terminal objects representing a house and a house with a chimney. The poly-functional intermediate objects are the objects represented by vertices which exists within a construction pathway of the house and within a construction pathway of the house with the chimney. Thus, if a consumer requests a chimney in a house after a firm has constructed the house without a chimney, the firm can add the chimney to the house by backtracking along the construction pathway of the house to a poly-functional intermediate object and proceeding from the poly-functional intermediate object along a construction pathway of the house with a chimney. [0108]FIG. 7 provides a flow diagram [0109] In step [0110] Alternatively, step [0111] The method of FIG. 7 for locating and selecting poly-functional intermediate objects for a set of terminal objects [0112] As indicated by the preceding discussion, the present invention identifies and selects the poly-functional object which leads to process redundancy to achieve reliability and adaptability. Specifically, a firm should ensure that there is an adequate inventory of the selected poly-functional object to enable the firm to adapt to failures and changes in the economic environment. [0113] Fitness Landscape [0114] The Analysis Tools [0115] More formally, a landscape is defined over a discrete search space of objects X and has two properties: [0116] (1) Objects xεX have a neighbor relation specified by a graph G. The nodes in G are the objects in G with the edges in G connecting neighboring nodes. G is most conveniently represented by its adjacency matrix. [0117] (2) A mapping f: X→R gives the cost of every object xεX. For purposes of simplicity, the cost is assumed to be real but more generally may be any metric space. [0118] Without limitation, the following embodiments of the landscape synthesis and analysis features of the analysis component [0119] For example, without limitation, a fitness landscape can represent the job shop scheduling problem. As previously discussed, in the job shop scheduling problem, each machine at the firm performs a set of jobs. Each job consists of a certain ordered sequence of transformations from a defined set of transformations, so that there is at most one job running at any instance of time on any machine. The job shop scheduling problem consists of assigning jobs to machines to minimize the makespan. The set of all possible workable or non-workable schedules defines the configuration space for the job shop scheduling problem. The neighborhood relation can be defined as a permutation of the assignment of jobs to machines. Specifically, one way to define the neighborhood relation is to exchange the assignment of a pair of jobs to a pair of machines. For example if jobs a and b are assigned to machines [0120] The Analysis component [0121] Next, for most problems, only a small fraction of the fitnesses of the configuration space can be determined through actual observations or simulation because of the large size of the configuration space. The Analysis Component [0122] Next, simulation and observation are not deterministic. In other words, the simulation or observation of the same input parameter values may yield different outcomes. This problem may be attributed to limitations associated with the selection of input parameters, errors associated with the setting of input parameters and errors associated with the observation of input parameters and outcomes because of noise. The analysis component [0123] The Analysis component [0124] In step [0125] In step [0126] wherein
[0127] C [0128] In step [0129] The covariance matrix C [0130] The covariance matrix C [0131] Assuming the discrete variables x are binary variables b, note that C [0132] where c C [0133] Now it is well known that the Hadamard product of positive semi-definite matrices is itself positive semi-definite as indicated by the Schur product theorem. Thus if we can show that C [0134] Note first that the matrix elements C [0135] Thus we can write the matrix C [0136] where 1 is the vector of is and b [0137] where {tilde over (b)} [0138] Noting that 1=b [0139] Diagonalizing this quadratic form we find that
[0140] which is clearly non-negative as long as |ρ|≦1. [0141] In an alternate embodiment, the covariance function is extended to include input dependent noise, Θ [0142] In step [0143] using the conjugate gradient method. However, as is known in the art, the method can use any standard optimization technique to maximize the logarithm of the likelihood function. As is known in the art, the gradient of the logarithm of the likelihood function can be determined analytically. See M. N. Gibbs. [0144] Since the determination of the values of the hyper-parameters, Θ=(Θ [0145] Preferably, P(Θ), the prior probability distribution of the hyper-parameters Θ=(Θ [0146] Next, step σ [0147] In the preceding two equations, y is a d-vector of previously observed outputs given by y′=(y [0148] C [0149] The following example shows the results obtained by executing the landscape synthesis method [0150] Execution of the discrete fitness landscape synthesis method
[0151] The determination of the hyper-parameters Θ=(Θ [0152] The analysis component [0153] We assume a data set D consisting of vector output values τ={t [0154] Before parameterizing matrix covariances functions suitable for regression on vector outputs we derive formulas which predict the y value at a previously unseen x. [0155] The task at hand is to predict the outputs y [0156] We recall that τ is a vector of length M×D given by τ=Σ [0157] To determine the probability distribution for t [0158] where K is the (M×D)×M matrix K=Σ [0159] It is convenient for our purposes to use the matrix inversion lemma to rewrite the 1,1 matrix element of the inverse so that
[0160] This result can now be used to simplify the argument in the exponential of Eq. (8) to
[0161] where cst is a term independent of t [0162] Thus the predicted values, {circumflex over (t)} {circumflex over (t)} [0163] [0164] where we recall the definition τ=Σ [0165] With these results all the standard techniques (e.g. determination of or integration over hyper-parameters) for scalar output GP can naturally be extended to the case of vector outputs. [0166] With these results, we now need to parameterize a useful family of M×M covariance functions of M objectives. The most natural covariance matrix function to pick is the matrix generalization of the scalar representations. For example, for multiple landscapes defined over bitstrings we might use
[0167] where the Greek indices label all possible
[0168] pairs of landscapes. Viewed as an M×M matrix for a fixed pair of input points the matrix C represents the covariances across the different objectives. Thus, it must be positive semi-definite. Let C C [0169] where (Θ [0170] To implement GP over landscapes we can maximize the log likelihood function directly to determine a maximum likelihood estimate of Θ and use this Θ for prediction. However, the log likelihood function is usually multi-modal and gradient ascent on the log likelihood is easily trapped on local maxima. Consequently, it is usually better to add a regularizing term through a prior P (Θ). We supply some tunable prior distributions that can be used for this purpose. [0171] The parameters in the covariance function Θ [0172] A common distribution used to parameterize positive variables is the gamma distribution.
[0173] The hyper-parameters α and β control the position and shape of the distribution. In terms of the mean m and and variance ν of the distribution α= [0174] For numerical stability in maximizing the posterior probability it is convenient to write this distribution in terms of variables which range over the entire real line. Consequently, we set Θ=exp [θ] and determine the distribution over θ as
[0175] Since we wish to maximize the logarithm of the posterior probability we note for completeness that
[0176] Another useful prior over positively constrained hyper-parameters is the inverse gamma distribution. The inverse gamma distribution is
[0177] The α and β parameters given in terms of the mean and variance are:
[0178] Transforming to coordinates θ=log Θ which range over the entire real line we then have
[0179] The logarithm of the prior probability in this case is
[0180] The ρ parameters are constrained to lie in |ρ|<1. Most often ρ is positive so we consider this special case before presenting a general prior. [0181] For positively constrained landscapes (so that 0≦ρ<1) like those generated by the NK model an appropriate prior over the ρ variables is a beta distribution:
[0182] The α and β parameters are determined in this case as
[0183] Again we transform coordinates so that the real line is mapped to the unit interval. In this case we write Θ as a sigmoid function of θ: Θ=(1+exp[−θ]) [0184] The log prior probability in this case is
[0185] When we need to include the possibility of negative ρ we can modify the Beta distribution to cover the interval Θε[−1,1] so that
[0186] The mean and variance of this distribution are m=(α−β)/(α+β) and v=4αβ/((α+β [0187] It is also useful to convert to a variable θ which assumes values over the entire real line. This can be accomplished by defining θ through Θ=tan h θ. The θ distribution is then
[0188] with α and β given as above. The log prior probability in this case is
[0189] The Analysis component [0190] The sparse bases techniques of the present invention compress the information in a landscape into a manageable size. In order to make use of landscapes, there must be a way to represent them with a concise description. Even for a landscape defined over bit strings of length n=20 there are over 106 pieces of information needed to completely specify the landscape. Moreover, a complete description of the landscape is usually exponential in the parameters of the landscape. For example, the information necessary to describe a n=30 landscape is 1000 times larger than the already large n=20 landscape. Accordingly, landscapes must be represented by a concise, compressed description to serve as a useful technique for operations management. [0191] The sparse bases techniques also characterize landscapes to identify the salient features of a class of landscapes. This characterization is useful because the optimization algorithms within the optimization component [0192] United Sherpa [0193] The sparse bases techniques also allow smoothing of landscapes which are polluted with noise such as intrinsic noise and noise introduced by measurement. Specifically, the analysis component [0194] The sparse bases techniques also achieve a multi-resolution description. In other words, the bases extracted for the landscape describe the structure of the landscape in many ways for use by the optimization component [0195] To determine sparse representations of landscapes the analysis component [0196] The basis φ={φ [0197] In the first approach, the analysis component [0198] To form the complete and orthogonal basis φ, the analysis component [0199] The complete and orthogonal basis φ is called the principle component basis. The small number of φ vectors having the largest eigenvalues suffice to capture most of the features of R. In other words, n<<|χ| so f defines a small subspace of R [0200] Many algorithms are known in the art to diagonalize a matrix to find the eigenvalues. Preferably, for large matrices, the analysis component [0201] After the analysis component [0202] Preferably, the basis is ordered in decreasing order of the eigenvalues. From a computational viewpoint, finding these n basis vectors is considerably simpler than diagonalizing the entire |χ|×|χ| correlation matrix R [0203] The principal component analysis representation of f offers a number of advantages. First, it is uncorrelated in the sense that
[0204] Moreover the principal component analysis reconstruction using m<n basis vectors
[0205] has the minimum squared error The final advantage is that the principal component analysis basis is compact or sparse. Specifically, the principal component analysis basis has a much lower dimension since m<<|χ|. [0206] In the second and preferred approach for determining the bases φ={φ [0207] In step [0208] to determine a, where λ=2βσ [0209] When minimized, the function S biases the a [0210] The sparse bases method [0211] represents a squared error criterion for the reconstruction error. The balance between sparseness and accuracy of the reconstruction is controlled by a parameter A. Larger values of λ favor more sparse representations while smaller λ favor more accurate reconstructions. In step [0212] The mathematical derivation of the energy function used in step [0213] Given a basis φ, the likelihood of obtaining a landscape f is [0214] and so, [0215] Thus, P(φ), P(a) and P(f|a, φ) have to be expressed. [0216] Since the landscapes are identical and independently distributed, the prior P(a) on the expansion coefficients is written as, P(a)=Π [0217] Alternatively, with a little extra complexity, a different β is used for each basis function φ [0218] This derivation assumes that the factors contributing to f after the correct basis has been determined are independent. The function S(.) is a function forcing the a [0219] Since the landscapes are generated by an independent and identically distributed process, the likelihood function can be written:
[0220] where f [0221] Thus, the coefficients are selected to minimize the least squared error. Further, the maximum likelihood estimate for φ is:
[0222] The maximum log-likelihood estimate, which is simpler to work with, is:
[0223] Substituting the specific forms for P(a) and P(f|a, φ) educes to minimizing an energy function which is used in tep [0224] where λ=2βσ [0225] Optimization [0226] The analysis component [0227]FIG. 10 shows the flow diagram of an overview of a first technique to identify a firm's regime. In step [0228] The definition of avalanches of alterations include a series of changes which follow from an initial change to a firm's operations management. For example, a firm makes an initial change to its operation management to adjust to failures or changes in its economic environment. This initial change may lead to further changes in the firm's operations management. Next, these further changes may lead to even further changes in the firm's operations management. [0229] In the first regime called the ordered regime, the initial change to a firm's operations management causes either no avalanches of induced alterations or a small number of avalanches of induced alterations. Further, the avalanches of induced alterations do not increase in size with an increase in the size of the problem space. [0230] In the second regime called the chaotic regime, the initial change to a firm's operations management causes a range of avalanches of induced alterations which scale in size from small to very large. Further, the avalanches of induced alterations increase in size in proportion to increases in the size of the problem space. [0231] In the third regime called the edge of chaos, the initial change to a firm's operations management causes a power law size distribution of avalanches of induced alterations with many small avalanches and progressively fewer large avalanches. Further, the avalanches of induced alterations increase in size less than linearly with respect to increases in the size of the problem space. The edge of chaos is also called the phase transition regime. [0232] The analysis component [0233] These algorithms must properly tune the scale of their modifications in order to achieve the desired improvement in the fitness of the firm's operations management. For instance, if the scale of the modifications of the natural experiments or purposeful experiments is too small, the firm will remain frozen in a region of the space of operations management solutions which is too small. Conversely, if the scale of the modifications of the natural experiments or purposeful experiments is too large, the firm will become too chaotic to adapt well to failures and changes in its economic web. However, if the scale of the modifications of the natural experiments or purposeful experiments is well tuned, the firm will search the space of operations management solutions efficiently and will settle into an optimal solution. [0234] The algorithms to improve the fitness of a firm's operations management are applicable to both single objective optimization and multi-objective optimization. For multi-objective optimization with n component fitness functions, the algorithms attempt to attain a Global Pareto Optimal solution. In a Global Pareto Optimal solution, none of the component fitness functions can be improved without adversely effecting one or more other component fitness functions. If the attainment of a Global Pareto Optimal solution is not feasible, the algorithms attempt to find a good Local pareto Optimal solution. In a Local Pareto Optimal solution, none of the component fitness functions can be improved by an incremental modification to a neighboring operations management solution without adversely effecting one or more of the other component fitness functions. The definition of optimal includes good solutions which may not necessarily be the best solution. [0235] An algorithm for improving the fitness of a firm's operations management is described in a co-pending provisional patent application, No. 60/103,128, titled, “A Method and System for Optimization of Operations Management using Production Recipes and Learning Curves” filed Oct. 2, 1998, the contents of which are herein incorporated by reference. [0236] Additional algorithms for improving the fitness of a firm's operations management involving local reinforcement learning with patches, neighborhood definition and limits on the fraction of components (tau) which can change at a particular times are described in co-pending provisional application titled, “Method and system for Dynamic Load-based Control of Routing in Data Communication Networks and of Control of Other Systems” (Attorney Docket Number 9392-0023-888) the contents of which are herein incorporated by references. These algorithms are further described in co-pending provisional application, No. 60/118,174, titled, “A Method and System for Adaptive, Self-Configuring Resource Allocation in Distributed Systems”, the contents of which are herein incorporated by reference. [0237] Fitness landscapes fall into three major categories in accordance with the characteristics of the landscape. FIG. 11 shows the flow diagram of an algorithm [0238] In the first category, none of the solutions represented on the fitness landscape representation of the operations management problem are acceptable solutions. In the second category, the fitness landscape representation contains isolated areas of acceptable solutions to the operations management problem. The second category is called the isolated peaks category. In the third category, the fitness landscape representation contains percolating connected webs of acceptable solutions. The third category is called the percolating web category. [0239] In step [0240]FIG. 12 [0241] In step [0242] The method [0243] Alternative techniques could be used to characterize fitness landscapes such as techniques which measure the correlation as a function of distance across the landscape. For example, one such technique samples a random sequence of neighboring points on the fitness landscape, computes their corresponding fitness values and calculates the auto-correlation function for the series of positions which are separated by S steps as S varies from 1 to N, a positive integer. If the correlation falls off exponentially with distance, the fitness landscape is Auto-Regressive [0244] Exemplary techniques to characterize landscapes further include the assessment of power in the fitness landscape at different generalized wavelengths. As is known in the art, the wavelengths could be Walsh functions. [0245] In step [0246] Without limitation, the algorithm of FIG. 11 for moving a firm to more desirable category of operation is described in the illustrative context of moving the firm to the percolating web category. However, it will be apparent to one of ordinary skill in the art that the algorithm of FIG. 11 could also be used to move the firm to the isolated peaks regime within the context of the present invention which includes the creation and landscape representation of the environment, the characterization of the landscape representation, the determination of factors effecting the landscape characterization and the adjustment of the factors to facilitate the identification of an optimal operations management solution. Step [0247] Preferably, step [0248] Easing constraints and improving the overall fitness for operations management produce a phase transition from the isolated peaks category to the percolating web category as explained by analogy to a physical landscape. Picture the landscape representation as the Alps with a cloud layer which begins at the valley and rises to a particular height. The area above the cloud layer in the sunshine on the Alps corresponds to the subspace of acceptable solutions on the fitness landscape. The area in the cloud layer on the Alps corresponds to the unacceptable solutions on the fitness landscape. Further, assume in the analogy that a hiker is on the Alps. Assume that the hiker remains alive in the sunshine and dies either immediately after entering the cloud layer or after lingering in the cloud layer for a particular time period. [0249] The first category of fitness landscapes corresponds to the situation where the cloud layer rises to a height above Mount Blanc, the highest point on the Alps. In this situation, the hiker cannot leave the cloud layer and dies. Accordingly, there are no acceptable solutions in the first category of fitness landscapes. [0250] Easing constraints and improving the overall fitness for operations management causes a phase transition to the situation where a small number of high peaks on the Alps lies above the cloud layer in the sunshine. In other words the easing of constraints and the improvement of the overall fitness act to lower the cloud layer and raise the landscape in the analogy. In this situation, the hiker lives if he remains on one of the high peaks which lie in the sunshine. However, the hiker cannot travel from one of the high peaks to another of the high peaks because he must pass through the cloud layer to travel between high peaks. Accordingly, the second category of fitness landscapes contains isolated areas of acceptable solutions. [0251] Continued easing of constraints and improvement of the overall fitness for operations management causes a phase transition to the third category of fitness landscapes corresponding to the situation where the cloud layer is sufficiently low and the landscape is sufficiently high to enable the development of connected or percolating pathways in the sunshine among the peaks. Accordingly, the third category of fitness landscapes contains connected pathways of acceptable solutions. [0252] The movement to the third category of fitness landscapes represents a movement to a operations management solution which is more reliable and adaptable to failures and changes in the economic web respectively. For example, suppose that failures and changes in the economic web cause a shift in the fitness landscape underneath the hiker. If the hiker is operating in an isolated peaks category, the hiker will be plunged into a cloud and die. Conversely, if the hiker is operating in a percolating web category, the hiker can adapt to the failures and changes by walking along neighboring points in the sunshine to new peaks. [0253] In the hiker analogy, the hiker represents a firm. The changing landscape represents changes in the economic environment of the firm. A hiker remaining in the sunshine represents a firm that can adapt to failures and changes in the economic environment while a hiker who falls into the clouds represents a firm that does not survive with changes in the economic environment. [0254] The optimization component [0255] Without limitation, the density estimation and extrapolation optimization method [0256] In step Y=␣I [0257] The intervals may overla [0258] Preferably, step [0259] In step [0260] Representing an input sequence space as x=x [0261] where {x P(x [0262] Such expansions assuming the {x [0263] The approach for estimating the probability density function P(x [0264] In step {p [0265] The number of lags of the standard lag method of step [0266] In step [0267] In step [0268] The discrete fitness landscape synthesis method [0269] Automated Market [0270] The AM [0271] Without limitation, the Automated Market [0272] Additional exemplary contexts for Automated Markets [0273] The AM [0274] In the preferred embodiment, the AM [0275] In the preferred embodiment, the computational agents utilize one or more of a variety of techniques to determine optimal buying or selling strategies for the corresponding economic agent. These techniques include fixed algorithms and evolving algorithms. The techniques include algorithms such as genetic algorithms, genetic programming, simulated annealing, and adaptive landscape search algorithms. These algorithms operate in either a fixed strategy space or in an open but algorithmically specifiable strategy space. The algorithms search for buy or sell strategies which optimize either single or multiple utilities within the economic agents. [0276] In the automated market [0277] In the present invention, computational agents searching trade strategy space can be tuned in a variety of means in automated markets [0278] Preferable, the Automated Market [0279] Similarly, the Automated market [0280] In the preferred embodiment, the AM [0281] For the exchange of goods, these terms include price and quantity. optionally, the terms could further include exchange location, exchange time, quality/purity descriptors, the current sequence of contracts, sales offers, and purchase offers and the future sequence of contracts, sales offers and purchase offers. For example, in the exchange of crude oil, the terms might include price, volume, delivery point, sulfur content, and specific gravity. The terms could also be contingent on the delivery of other contracts. [0282] For the exchange of services, the terms include at least price and time. Further, the terms could also include other factors which are necessary to specify the service. For example, in the exchange of transportation services, the terms would include price, volume, weight, pickup time and location, and delivery time and location. [0283] The Automated Market [0284] In general, there will be more than 3 terms that need to be negotiated on a particular exchange. When there are more than three terms, it will not be easy to visualize the preference surface. In this case, the preference surface is entered into the automated market [0285] The automated market [0286] Buyer and seller surfaces scheduled for reconciliation at the time of a matching are committed. In other words, each buyer and seller is committed to accept any trade below or above their preference surfaces respectively. The automated market [0287] The automated market [0288] After analysis of a given matching period, the automated market [0289] As previously explained, the automated market [0290] In the most general setting we must optimize over many terms including prices p and volumes v to maximize the joint satisfaction. Without limitation, the Automated Market [0291] and the Automated Market [0292] Any transaction may involve multiple stocks. If the link trader cares only about total costs, and there are n stocks, the total cost c to the link trader is
[0293] Buying stock corresponds to positive volumes, v [0294] The satisfaction profile for the link trader can be entered by the user by specifying the satisfaction at a set of m., distinct points {(C [0295] where 1≦α≦m ∂ [0296] The satisfaction of the contra traders is defined next. The Automated Market [0297] where 1≦α≦m v [0298] The joint satisfaction of all traders S [0299] Using Eqs. (10) and (13) in Eq. (9), the optimization task is to determine:
[0300] If S [0301] where s [0302] In this form it is evident that the only coupling between the p [0303] From Eqs. (11) and (12): ∂ [0304] so that a solution ∇ [0305] Next, a possible minimization algorithm based on a decomposition method is described. The joint satisfaction S(p|v)=s [0306] where the new coordinates are x [0307] minimize
[0308] subject to
[0309] The only coupling between variables comes through the constraint. Introducing a single Lagrange multiplier for the constraint the Lagrangian for this problem is
[0310] where L [0311] For a given λ, say λ maxL(x(λ) λ)≡maxq(λ). [0312] Maximizing this function using steepest ascent requires the gradient of the dual function q(λ):
[0313] As noted in the last step since x λ [0314] where α is the step size. This algorithm will converge to a local λ peak. [0315] It may be the case that q(λ) is not a convex function, but we know that for the global optimum of the constrained problem the multiplier λ* satisfies λ*=arg max [0316] so that a global optimization technique like simulated appealing could be used to determine λ* and thereby the globally optimal x. Note that the dual function q(λ) is not a direct function of λ but indirect through the determination of x(λ). Fortunately, x(λ) can be evaluated extremely rapidly in parallel. Also, it may be the case that q(λ) is convex. [0317] The efficiency of the above method requires quick optimization of L [0318] The satisfaction function of the ith trade is represented analytically as a Fermi function, s [0319] This function is minimized by
[0320] Once β and μ have been estimated the above formula will serve as a good starting point for a Newton's method. [0321] The next natural extension is the case in which volumes are not fixed but are also optimized along with the price. The problem remains the same except that now the constraint is a quadratic function of the variables. As is known in the art, there are a number of obvious ways to extend Lagrangian relation. In the preferred embodiment, we need to minimize S(p,v) where we have an effective tool to minimize S(p,v) for any fixed volume. Thus, a general technique to solve the general problem might be to initialize some guess for v and then solve for the best prices. At that new point (p,v), calculate the gradient ∇,S(p,v) and update the volumes accordingly, e.g. by steepest descent v [0322] An application of the automated market [0323] Allowing for two-way bidding, the automated market [0324] While the application of the automated market [0325]FIG. 13 [0326] The storage system [0327] The producer communication system [0328] 1. Material type (with check boxes for special handling requirements); [0329] 2. Maximum total volume available at point A; [0330] 3. Minimum volume to ship from point A; [0331] 4. Earliest pickup time from point A (Later, this could be specified as a list of times and volumes available at those times.) [0332] 5. For each destination (B, E, F): [0333] a) Minimum worthwhile volume to that destination; [0334] b) Maximize volume to that destination; [0335] c) Latest delivery time for that destination (Again, this could be specified as a list of acceptable delivery times and acceptable volume ranges.) [0336] In addition, the producer would specify the maximum price acceptable for any of the combinations of transportation services that meet the requirements above. Producer prices can be entered as mathematical formulas which depend on several factors, for example: [0337] 1. Volume to ship to each destination; [0338] 2. Weight to ship to each destination; [0339] 3. Pickup time; [0340] 4. Delivery time. [0341] The producer communication system [0342] The producer communication system [0343] The service provider communication system [0344] The offer fill-out forms displayed at terminals [0345] 1. Volume to ship; [0346] 2. Weight to ship; [0347] 3. Time to ship; [0348] 4. Distance to ship. [0349] Also, when a vehicle is used on a return-route, under consideration are the incremental distance to perform the service (the distance between the place where the vehicle becomes available after satisfying a previous obligation and the place where the current service starts at) and the incremental time to perform the service. [0350] In addition, other factors, such as the number of nights and the number and type of border crossing, could be included for the total journal, the actual shipment, or on an incremental basis. [0351] The service provider communication system [0352] The service provider communication system [0353] The central hub [0354] 1. Price per truck-mile (the higher the price, the higher the priority;) [0355] 2. Route length (the longer the length, the higher the priority;) and [0356] 3. Time of request submission (the earlier the time, the higher the priority.) [0357] After the examination, the request ranking system [0358] The offer selecting system [0359] The matching system [0360] 1. Price per truck-mile (the lower the price, the higher the priority;) [0361] 2. Route length (the longer the length, the higher the priority;) and [0362] 3. Time of request submission (the earlier the time, the higher the priority.) [0363] After examining these offers, the matching system [0364] The contracting system [0365]FIG. 13 [0366] After login by a user, the automated market [0367] In step [0368] Similarly, in step [0369] In step [0370] Similarly, in step [0371] In step [0372] In step [0373] After step [0374] In step [0375] Resource Allocation [0376] The present invention further comprises a method and system to allocate resources using technologies graphs, passive and active searching, reinforcement learning, market driven decision making, reinforcement learning as well as p, tau, and patches techniques. [0377] Without limitation, the following embodiments of the present invention are described in the illustrative context of the allocation of resources in a distributed, computing system. However, it will be apparent to persons of ordinary skill in the art that other contexts can be used to embody the aspects of the present invention. These aspects, which are applicable in a wide range of contexts include receiving a plurality of resource requests and a plurality of resource offers, determining at least one relation between the resource requests and the resource offers to identify matching resource requests and offers and allocating the resource offers to its matching resource request. [0378] System Architecture [0379]FIG. 14 shows an illustration of the architecture of the system of the present invention. The system includes resource requests [0380] The system of the present invention further includes resource offers [0381] The system of the present invention further includes resource providing nodes (RPNs) [0382] Resource Allocation Method [0383]FIG. 15 provides a flow diagram describing a method [0384] In step [0385] Execution of step [0386] Searching for Relations [0387] In one embodiment, the method of the present invention for allocating resources searches for relations between the resource requests [0388] In another embodiment, the method of the present invention for allocating resources searches for relations between the resource requests [0389] Market-Based Resource Allocation [0390]FIG. 16 displays a flow diagram of a method [0391] In the context of the market-based allocation method [0392] The contracted amount is paid in full only if the resource request [0393] The bids for resource requests [0394] In step [0395] In step [0396] Each resource providing node [0397] Resource providing nodes [0398] When reinforcement learning is used to adjust the behavior of resource providing nodes [0399] Each resource providing node [0400] Resource requests [0401] A market-based allocation method for data routing is explained in co-pending international patent application number PCT/US 00/02011, filed Jan. 28, 2000, and titled, “A Method and System for Routing Control in Communication Networks and for System Control”, the contents of which are herein incorporated by reference. [0402] Locally-cooperative local reinforcement learning [0403] Having all resource providing nodes [0404] Accordingly, in the preferred embodiment the present invention utilizes combinations of the following three semi-local strategies: [0405] patches In this technique, resource providing nodes [0406] p A neighborhood is defined for a resource providing nodes [0407] tau Only a fraction (called tau) of the resource providing nodes [0408]FIG. 17 provides a flow diagram [0409] In the preferred embodiment, the present invention uses either patches or p or both to define a modified reward and hence, a return, for a resource providing nodes [0410] Preferably, the parameters for these strategies (the fraction p, the fraction tau and the number and membership of patches) are global in nature. In other words, the values of these parameters are the same for all resource providing nodes [0411] Preferably, the present invention sets these parameters as follows: [0412] First, a global performance measure is defined. Preferably, the global performance measure is the specified quality of service in relation to the quality of service of the satisfied resource request [0413] Preferably, each value of the global parameters governing p, patches, tau, and reinforcement learning features, defines a point in the global parameter space. With respect to this point, the method for allocating resources of the present invention achieves a given global fitness. The distribution of global fitness values over the global parameter space constitutes a “fitness landscape” for the entire bandwidth-agent system. Such landscapes typically have many peaks of high fitness, and statistical features such as correlation lengths and other features as described in co-pending international patent application number PCT/US 99/19916, titled, “A Method for Optimal Search on a Technology Landscape”, the contents of which are herein incorporated by reference. In the preferred embodiment, these features are used to optimize an evolutionary search in the global parameter space to achieve values of p, patches, tau, and the internal parameters of the reinforcement learning algorithm that optimize the learning performance of the resource allocation system in a stationary environment with respect to load and other use factor distribution. Preferably, the same search procedures are also used to persistently tune the global parameters of the resource allocation system in a non-stationary environment with respect to load and other use factor distributions. [0414] By tuning of the global parameters to optimize learning, the present invention is “self calibrating”. In other words, the invention includes an outer loop in its learning procedure to optimize learning itself, where co-evolutionary learning is in turn controlled by combinations of p, patches, and tau, plus features of the reinforcement learning algorithm. The inclusion of features of fitness landscapes aids optimal search in this outer loop for global parameter values that themselves optimize learning by the resource allocation system in stationary and non-stationary environments. [0415] Use of p, tau, or patches aids adaptive search on rugged landscapes because, each by itself, causes the evolving system to ignore some of the constraints some of the time. Judicious balancing of ignoring some of the constraints some of the time with search over the landscape optimizes the balance between “exploitation” and “exploration”. In particular, without the capacity to ignore some of the constraints some of the time, adaptive systems tend to become trapped on local, very sub-optimal peaks. The capacity to ignore some of the constraints some of the time allows the total adapting system to escape badly sub-optimal peaks on the fitness landscape and thereby, enables further searching. In the preferred embodiment, the present invention tunes p, tau, or patches either alone or in conjunction with one another to find the proper balance between stubborn exploitation hill climbing and wider exploration search. [0416] The optimal character of either tau alone or patches alone, is such that the total adaptive system is poised slightly in the ordered regime, near a phase transition between order and chaos. See e.g. “At Home in the Universe” by Kauffman, Chapters 1,4, 5 and 11, the contents of which are herein incorporated by reference and “The Origins of Order”, Stuart Kauffman, Oxford University Press, 1993, Chapters 5 and 6, the contents of which are herein incorporated by reference. For the p parameter alone, the optimal value of p is not associated with a phase transition. [0417] Without limitation, the embodiments of the present invention are described in the illustrative context of a solution using tau, p, and patches. However, it will be apparent to persons of ordinary skill in the art that other techniques that ignore some of the constraints some of the time could be used to embody the aspect of the present invention which includes defining an algorithm having one or more parameters, defining a global performance measure, constructing a landscape representation for values of the parameters and their associated global performance value, and optimizing over the landscape to determine optimal values for the parameters. Other exemplary techniques that ignore some of the constraints some of the time include simulated annealing, or optimization at a fixed temperature. In general, the present invention employs the union of any of these means to ignore some of the constraints some of the time together with reinforcement learning to achieve good problem optimization. [0418] Further, there are local characteristics in the adapting system itself that can be used to test locally that the system is optimizing well. In particular, with patches alone and tau alone, the optimal values of these parameters for adaptation are associated with a power law distribution of small and large avalanches of changes in the system as changes introduced at one point to improve the system unleash a cascade of changes at nearby points in the system. The present invention includes the use of local diagnostics such as a power law distribution of avalanches of change, which are measured either in terms of the size of the avalanches, or in terms of the duration of persistent changes at any single site in the network. [0419] The present invention's use of any combination of the above strategies, together with reinforcement learning in any of its versions, give it an advantage over prior art resource allocation methods because these strategies address many problems that could arise including the following: [0420] slow convergence to optimal allocation patterns, [0421] oscillation of network load, and [0422] locally beneficial but globally harmful routing patterns. [0423] Without limitation, the embodiments of the present invention have been described in the illustrative context of a method for allocating resources. However, it is apparent to persons of ordinary skill in the art that other contexts could be used to embody the aspect of the present invention which includes defining an algorithm having one or more parameters, defining a global performance measure, constructing a landscape representation for values of the parameters and their associated global performance value, and optimizing over the landscape to determine optimal values for the parameters. [0424] For example, the present invention could be used for operations management. The present invention, using agents to represent resources and operations in the enterprise model, coupled to reinforcement learning, p, patches and tau, is used advantageously to create a model of a learning organization that learns how to adapt well in its local environment. By use of the outer loop described above, good global parameter values for p, patches, tau, and the reinforcement learning algorithm are discovered. In turn, these values are used to help create homologous action patterns in the real organization. For example, the homologous action patters can be created by tuning the partitioning of the organization into patches, by tuning how decisions at one point in the real organization are taken with respect to a prospective benefit of a fraction p of the other points in the organization affected by the first point, and by tuning what fraction, tau, of points in the organization should try operational and other experiments to improve performance. [0425] In addition, the distribution of contract values and rewards in the reinforcement algorithm can be used to help find good incentive structures to mediate behavior by human agents in the real organization to achieve the overall adaptive and agile performance of the real organization. [0426] In addition to the use of the invention to find good global parameters to instantiate in the real organization, the same invention can be used to find good global parameter values to utilize in the model of the organization itself to use that model as a decision support tool, teaching tool, etc. [0427] Further, the present invention is also applicable to portfolio management, risk management, scheduling and routing problems, logistic problems, supply chain problems and other practical problems characterized by many interacting factors. [0428] Minimizing Values at Risk [0429] The present invention includes techniques to minimize the value at risk of a portfolio. Value at risk is a single, summary, statistical measure of possible portfolio losses. Specifically, value at risk is a measure of losses due to “normal” market movements. Losses greater than the value at risk are suffered only with a specified small probability. [0430] Using a probability of x percent and a holding period of t days, a portfolio's value at risk is the loss that us expected to be exceeded with a probability of only x percent during the next t-day holding period. [0431] The technique to minimize the value at risk uses historical simulation. Historical simulation is a simple, atheoretical approach that requires relatively few assumptions about the statistical distributions of the underlying market factors. In essence, the approach involves using historical changes in market rates and prices to construct a distribution of potential future portfolio profits and losses, and then reading off the value at risk as the loss that is exceeded only x percent of the time. [0432] The distribution of profits and losses is constructed by taking the current portfolio, and subjecting it to the actual changes in the market factors experienced during each of the last N periods. That is, N sets of hypothetical market factors are constructed using their current values and the changes experienced during the last N periods. Using these hypothetical values of market factors, N hypothetical mark-to-market portfolio values are computed. From this, it is possible to compute N hypothetical mark-to-market profits and losses on the portfolio. [0433] The following discussion describes the technique for isolating low value at risk portfolios. Let us consider a single instrument portfolio, in this case stocks traded on the New York Stock Exchange and Nasdaq markets. For this instrument, there exists tremendous amounts of data. If we assume a one day time horizon (t=1), then the data we are interested in are the daily closing prices of every publicly traded stock on the two markets. Such data exists for thousands of stocks for tens of thousands of days. From these data, it is possible to construct an m×n matrix (where mis the number of stocks, and n is the number of days) of prices. [0434] Let us assume that within this collection of stocks, there are pairs, triplets, quadruplets, etc., of stocks whose values at risk are lower as a group than any of the stocks individually. This occurs because sets of stocks whose price changes are anti-correlated will have low values at risk. When the price of one stock goes down, the price of the other tends to go up. The chance that both stocks go down together is lower than the chance that two stocks chosen at r˜random would go down together because the stocks are anti-correlated. This reduces value at risk. [0435] The optimal portfolio would group anti-correlated stocks in the optimal proportions to minimize value at risk. Because there are so many stocks, however, the space of all possible portfolios is too large to search exhaustively. Genetic algorithms are well suited to finding good solutions to just this type of problem in reasonable amounts of time. [0436] The algorithm works as follows: [0437] Step 1: [0438] Start with m portfolios. Each portfolio can be represented as a vector of length m. Each bit (m [0439] Step 2: [0440] Go back in time n/2 days (halfway through the data). For each of the m portfolios, compute the value at risk for the n12 days that precede the halfway point. [0441] Step 3: [0442] Randomly pair portfolios. For each pair of portfolios, let the portfolio with the higher value at risk copy half of the bits of the lower value at risk portfolio (i.e. randomly select half of the bits in the more successful portfolio. If a bit is different, the less successful portfolio changes its bit to match the more successful portfolio). The portfolio with the lower value at risk remains unchanged. [0443] Step 4: [0444] Repeat steps 2 and 3 until some threshold for value at risk is achieved. [0445] In this way, clusters of anti-correlated stocks will tend to spread through the population of portfolios. The hope is that this method will ultimately select for most or all of the good clusters. Notice that this method may also alight upon the optimal number of stocks to hold in a portfolio. For example, if the minimum value at risk portfolio contains only three stocks, three-stock portfolios will tend to propagate through the population. [0446] Additional Techniques for the Analysis of Risk [0447] The present invention includes additional techniques for the analysis of risk. The general understanding of portfolio risk requires an understanding of three contributing problems. The current understanding of these three problems is insufficient to accommodate the challenges posed by modem portfolios. The first problem is volatility. It has long been known that Gaussian approximations to volatility do not correctly describe the behavior of markets, and that price fluctuations show long tails. This means that large deviations are much more likely than conventional theory suggests. The second issue is that of interdependence. In many areas of interest, elements of a portfolio do not move independently of each other, but rather influence each other in ways both subtle and complex. Current methods only uncover a minimal rendering of this complex structure. The third issue is that of time dependence. Many portfolios contain elements that do not mature on the same time scale, but are nonetheless dependent. Again, conventional portfolio analysis and optimization techniques do not address the subtleties of interacting time scales. [0448] It was originally pointed out by Mandelbrot in [0449] In the construction of portfolios, it is experimentally known that the prices of certain stocks are correlated, and this correlation is typically measured using a covariance matrix. The covariance matrix has two implicit assumptions which we believe are wrong: Fluctuations in prices are Gaussian (see above) and correlations between stocks are describable with pair-wise interactions. The present invention modifies this approach in two ways: [0450] 1. The covariance matrix requires a large amount of data for accurate results. Extending the covariance matrix method to higher order interactions (three- or four-point interactions) requires an exponentially increasing amount of data. We separate the analysis of interdependence into effect and magnitude. The effect aspect is obtained by encoding price fluctuations as (+,−) instead of numerical values. Now we have prices encoded as binary strings instead of numerical sequences. Since the fundamental activities of a market are buying and selling, and their attendant effects are the raising and lowering of prices, we believe that the binary encoding is a more fundamental signature of market microstructure than the actual prices. The magnitude of gains and losses are obtained by the statistics of large numbers of players making “atomic” buy and sell decisions. [0451] Once we have encoded the market dynamics of individual instruments as bit strings, we have essentially a telegraph representation of information. This is amenable to the tools of information theory, a field developed to analyze the dynamics of information transfer in telephony. Information theory allows us to measure correlations at arbitrary levels of interconnectedness, and although the data requirements scale exponentially as interconnectedness increases, the constant in front of the exponent is much smaller than the covariance case because of the binary nature of the data. Interconnectedness is measured by a quantity called mutual information, and the assumptions associated with it are less stringent than the assumptions required to measure covariance, and in particular are not dependent on the assumption of a normal distribution. [0452] 2. The present invention uses the measure of mutual information to construct a phylogeny of interdependence, using the technique of minimal spanning trees coupled with higher order information correlations to remove degeneracies. (multiple solutions satisfying the same constraints) Once we have constructed a phylogeny (a tinkertoy like structure showing connections of influence), we can use time-structured data to obtain directions of influence. This directed map allows us to model the propagation of financial disturbance through a web of connections. This is an important tool for constructing a portfolio of minimum risk, because it decomposes portfolio risk into an ensemble of interconnected contributing factors which can then be optimized to obtain the desired results. [0453] Note: The connections discussed above can be endogenously or exogenously generated. If the portfolio in question consists of internal assets, (R&D portfolio, for instance) there is some control as to the degree and nature of the interconnection. Hence the optimization procedure is somewhat different, as the interconnections are no longer viewed as given but are now variables over which we have some control. [0454] Inmany areas of interest, different time scales are an important consideration. Examples include bonds with different dates of maturity, and internal portfolios with different payback profiles. Optimizing over such portfolios requires understanding the spectrum of possible paths that a portfolio can take over time, and again interdependencies and large fluctuations make standard approximations of Gaussian uncertainties inaccurate. The present invention uses techniques borrowed from non-standard statistics (large deviation theory, sampling theory) and quantum field theory (path integrals) to generate a forward curve of the behavior of a complex portfolio over time. Not only is the end result important, but the shape of the curve over time is important, as there are many quantities of interest which are dependent on the local shape of the curve. [0455] Evaluating and Minimizing Risk [0456] The present invention includes additional techniques for portfolio optimization using sampling and selection to evaluate and minimize risk for a portfolio of assets with uncertain returns. Consider the general setting in which a holder owns portfolio of assets. The assets may be financial instruments (such as stocks, bonds, options, or other derivatives) or investments in research and development with unknown future payoffs (e.g. the drug leads pursued by a pharmaceutical company). In this setting, where the future rewards are uncertain, there are two important concerns of the holder of the portfolio. Firstly, it is important to quantify the risk (the amount of money that could be lost) over some time horizon. Secondly, the holder wishes to structure the portfolio so as to minimize the risk. In this document we will focus on answering these questions for portfolios of financial instruments but the ideas are more generally applicable. [0457] Let x [0458] Furthermore let P(x′, t′|x, t) represent the probability that the asset prices are x′ at time t′>t given that the asset prices were x at time t. If t indicates the present time and x represents the present value of the assets then the expected value of the portfolio at some time t′ in the future is [0459] This value indicates the expected worth of the portfolio but does nothing to tell us what the risk is, i.e. what we might conceivably lose. To get at this quantity we realize that from P(x′, t′|x, t) we can also determine the probability P(v|t) that the value at. rime t is v: [0460] This probability is the fundamental quantity which allows us to assess risk since it gives the probabilities for all potential outcomes. Thus for example we can say things like “with 95% confidence the most money that will be lost, is v*.” In this case v* is determined from the requirement that only 5% of the time will more money be lost, i.e. ∫ [0461] Other measures of risk are similarly based on P(v|t). [0462] The risk will depend sensitively on the precise form of P(x′, t′|x,t). To see this, imagine that a pair of assets i and j are anti-correlated with each other (i.e. when the price x [0463] While traditional VaR captures pairwise variations in asset prices it completely ignores higher order relationships between variables, e.g. when assets i and j go up asset k goes down. Moreover the Gaussian assumption inherent in VaR is known to be false. What is needed is a more general approach. The present invention describes new risk management techniques which move beyond pairwise VaR. We propose two methods by which this can be accomplished. [0464] The core of the new ideas is to recognize that information about higher order relationships can be uncovered by looking at the VaR of subsets of assets from the portfolio. [0465] Imagine for a moment that a specific set of assets covaries with each other in some predictable way. Knowledge of this co-variation can be used to devise a risk averse combination of these particular stocks. Since the variation involves all four stocks it can never be determined by only looking at pairs of assets. How might important, clusters of stocks like these be discovered? [0466] The first point to note is that the historical record of asset prices and portfolio values provides a training set from which we can discover these clusters of stocks. The historical record provides a data set which includes the true VaR because the future value of the portfolio is known from the historical data. Let v represent the true VaR for a particular portfolio x at a point T into the future. From the historical record we can form the data set D={x [0467] We begin by making the simple assumption that P(v|x)=δ(−μ(x)), i.e., it is characterized entirely by its mean value μ(x). This mean value will differ for different subsets of stocks. Without much additional complication we could also include the variance around this mean and assume that fluctuations around the mean are Gaussian: P (v|x)=N (μ(x), σ [0468] Given that we can determine the true average VaR for any set of stocks we can identify those assets within a portfolio of N assets that form good combinations. Computationally the following scheme can be used to identify good subsets of assets. We assume that the optimal subset of assets is of size n<<N. Starting from the original portfolio randomly form portfolios of half the size by sampling stocks (without replacement) from the entire portfolio. The probability that any one of these randomly generated profiles contains all n assets is approximately ½ [0469] After m iterations of this procedure the portfolio size is N/2 [0470]FIG. 18 provides a flow diagram of a method for evaluating and minimizing risk. In step [0471] In step [0472] There are many variations to this basic method that might improve its efficiency. It may not be optimal to halve the portfolio size at each step since we might want to have a higher probability of retaining the subset intact. The best number of random portfolios to generate and test can also be fine-tuned to make the search more efficient. Simple analytical model can be built to optimize these algorithm parameters. [0473] Another important extension is to realize that the sample and select method outlined above can be used to determine subsets with any desired properties. Nothing in the above procedure restricts its use to minimize VaR. The method can be used to extremize any figure of merit. Along these lines what happens in practice is that there are actually more than one objective in designing a portfolio: we want to minimize risk but also maximize a profit. Is there a way of modifying the above procedure to account for more than a single objective? At the very least we must be able to balance risk/reward the way it is conventionally done. [0474] There is however a very simple extension of the above method to handle the case of multiple objectives. Sub-sampled portfolios are generated as usually but the selection criteria amongst portfolios is modified. Now Instead of picking sub-sampled portfolios which have the best VaRs we measure a number of objectives for each of the particular sub-sampled portfolio and keep those sub-sampled portfolios which Pareto dominate all other portfolios (generated at the present iteration or all previously generated portfolios). With this simple change in the selection criteria everything else proceeds the same way. At the end of the process we obtain a subset which is hopefully good on all objectives. [0475] There are also a host of issues involving sampling to form the new portfolios. In the above I have assumed that the sampling is uniform. of course this need not be the case. The distribution can be altered over time (much as in boosting) based on the VaR (or distributions over VaR) over previous samples. This also ties into pooling in that the samples may be constructed in a much more deterministic fashion. [0476] There are also a number of possible extensions based upon the fact that the mapping from the asset subset can be determined. At this point it remains unclear how to exploit this fact. The basic procedure might also have to be modified slightly to fit the context. [0477] One of the main difficulties of the procedure may occur in the initial phases of the algorithm. Initially we must filter out subsets that contain the desired small subset. There arises the signal/noise ratio issue on whether the small optimal subset can be detected in the larger portfolio. An estimate of this would be useful to know how many subset samples to generate. This has been tested and the results will be discussed below. [0478] In order to test the algorithm, a random number generator was used to create a “toy” set of stock data in which all of the clusters were known a priori. [0479]FIG. 19 provides the results of the method for evaluating and minimizing risk from executing on 500,000 random draws from this “toy” world. The world consists of 32 stocks in which an investor may invest. The solid square plot is a histogram of VaRs for portfolios which include exactly 16 stocks. The mean VaR for these portfolios is −0.96 (where negative means the investor loses money). The smaller histogram of hollow circles shows the VaRs only for those portfolios which, through random sampling, failed to include any of the good clusters. The mean for the no-stocks-in-clusters (SIC=0) portfolios is −1.08, clearly worse than for the population as a whole. This is exactly the signal we need to separate the good portfolios from the SIC=0 portfolios. [0480] At the next iteration, the best [0481] There are three features worth noticing in FIG. 20. First, the total number of portfolios that contain zero stocks in clusters has increased dramatically. This is because as you remove stocks, your chance of breaking up clusters increases exponentially. Second, the entire distribution has shifted to the left, meaning that the VaRs have gotten worse (mean−1.66). This is because their is value in diversity and diversity must decrease as portfolio size goes down. Third, the distribution of SIC=0 portfolios is still worse (mean=−1.72) than the distribution of all portfolios. [0482] This third feature allows the modeler to determine the number of child portfolios necessary to ensure that some children still contain intact clusters. The number of children containing clusters is negatively binomially distributed as
[0483] where n is the number of children, r is the number of children that contain clusters, and p is the probability of randomly selecting a portfolio which is to the right of the 99% confidence interval of the SIC=0 histogram. [0484] Using these facts, a preliminary model has been calibrated and successfully finds clusters on more than 95% of the runs. [0485] The present invention also includes a method for portfolio modification. There are other methods to try to identify beneficial changes to a portfolio. Traditional VaR theory measures the effects of modifying (i.e. increasing or decreasing the holding) a position in one of the assets. As we have seen, if higher order combinations of assets are important then the effects of a single asset might be minor There is an important practical reason why traditional VaR focuses on the changes of only a single asset. If the portfolio is of size N and we consider changes involving m assets then on the order of N [0486] In contrast, the present method determines the optimal number of assets to change while searching for an optimal portfolio. FIG. 21 displays a flowchart illustrating the method for portfolio modification. In step [0487] In step [0488] In this case, the landscape is defined over portfolios. The fitness is the VaR measurement of the portfolio and two portfolios are neighbors if they differ in the holding of a single asset. So for example a portfolio of five assets might be represented as [ [0489] Portfolio Management—Optimization of Risk-Revenue [0490] The present invention further includes additional techniques to optimize both risk and revenue for portfolio management. Portfolio optimization (management) normally includes two-fold problem, the control of risk which is usually associated with the volatility (and as it's recently understood with higher moments of the multivariate distribution of the return of the assets constituting the portfolio). This is a formidable problem and a variety of methods have been already proposed to treat it, mention for instance one-dimensional measures of risk in terms of the Value-at-risk (VAR). Also the problem of the so-called heavy tails has been discussed in the literature in order to manage higher order yet less probable risk. [0491] Mathematically the problem is reduced to minimization of the variance and kurtosis of the weighted sum
[0492] of specified stocks under the wealth condition [0493] In what we propose the problem posed is to first search for “good” families of stocks on the basis of all stocks available at the market. For instance a family of two stocks perfectly anticorrelated will simultaneously minimize variance as well as kurtosis (which defines the higher risk contribution) properly created from these stocks. Their image in the vector space of zero-mean deviations is and the angle is given by the correlation coefficient
[0494] with ||x [0495] x [0496] By calculating the correlation matrix of the stock available at the market we can pick up the families consisting of a reference vector (stock) “a father” and strongly anticorrelated with it members “sons”. These members could be statistically independent: ρ [0497] Given requirements for risk and return the techniques described allows to build up corresponding portfolios. The whole procedure is comprised of three phases: [0498] Search for good families. [0499] a) Creation of the correlation space, including shifted correlations. [0500] b) Looking for “anticorrelated” families. [0501] c) Checking robustness of the families. [0502] Building perspective portfolios [0503] a) Creating two, three and four stock clusters with minimal variance. [0504] b) Evaluating their risk [0505] Optimization of expected return (revenue) of a portfolio chosen from the perspective ones. [0506] The first most difficult part of the program has been checked on the samples consisting of 32 and 100 time series with intentionally created clusters in the first 32 of them. There were four 2-point clusters, four 3-point clusters, and three ρ [0507] and on FIG. 24, where are also shown lesser couplings between the clusters and some other series. As far as the method is geometrical in character, identification of clusters for the both samples ( [0508] Portfolio Risk Management Using Multi-Gaussian Distributions [0509] Portfolio Risk Management (PRM) is essentially based on the techniques applied to estimate probability of “bad events”. For that one should know the corresponding probability distribution. During several years after Black and Scholes in finance business became widespread the VAR machinery which used Gaussian distribution, even though market data showed quite noticeable deviation from it. As a result, the so-called problem of fat tails arose along with a new fashion to treat it on the ground of Levy distribution (later on the truncated Levy distribution). Underline there is no evident reason to regard the latter the only possible cure. Moreover even its relation to the problem from the market point of view, has still remained foggy. [0510] This invention addresses the problem by using a very well-known two-time scale formalism going back to Van der Paul and then Bogolubov and Mitropolskii. [0511] In our case the method gives rise to the two-way gaussian distribution (2-GD)
[0512] For the sake of simplicity we consider the symmetric distribution with zero mean and the normalization [0513] This approach allows us to represent market dynamics at a level of description intermediate between macroscopic modeling (one-factor models) and microscopic modeling (individual agent models or multi-factor models, general stochastic processes). Two-gaussian approach being the simplest exactly solvable model yet can catch certain specific features of the fat tails behavior. It describes two groups of investors acting at the market with different time scale behavior. [0514] For comparison we are using the standard normalized distribution (the Black-Scholes approach)
[0515] that implies the variance is measured in units of the σ. Let us consider the probability, sometimes called VAR, of negative deviations larger than a specified value “a”
[0516] and the difference
[0517] Since the first two moments are same for the both distributions the difference is proportional to the kurtosis of 2-GD (1):
[0518] It can be shown that under conditions (2) and (3) the difference while expanding in the series over moments is
[0519] This difference can be incorporated by the so-called implied volatility (variance) through considering the effective (implied) gaussian process
[0520] where σ [0521] In order to check out formula (8) we calculated δP by using the exact distribution ( [0522] where S is the current (stock) price of the underlying asset, S [0523] To make the model less restrictive one can consider three (and even more) gaussian distributions
[0524] with the normalization conditions Σn Σn Σn [0525] such that the difference
[0526] can be made proportional to any specified moment. [0527] Finally we give the next term in the expansion (8):
[0528] with
[0529] for the 2-GD. [0530] Infrastructure Design [0531] As explained throughout this application above, several new developments in the sciences of complexity, bear on an analysis of infrastructure robustness, and in particular on the probability of avalanching cascades of failures and means to achieve robust adaptive performance to avoid such “glitch” propagation. The present invention makes use of the following areas: [0532] As previously discussed, there is a general phase transition for hard to solve problems, ranging from job shop scheduling problems to military operations and their logistic support, to the functional fragility of interconnected nodes and flows in many infrastructures settings. The phase transition is a sudden shift from a robust, reliable regime to a fragile regime as one tunes the attempt to achieve ever-higher efficiency on one or more “figures of merit”. The general finding is that the insistence on too much efficiency removes redundancy from the system in question. In turn, this reduction in redundancy increases the conflicting constraints present in the operational system. In turn, at some point, this increase in conflicting constraints converts the problem from one that is easily solved, and robustly able to handle component failures by finding alternative neighboring solutions, to a system that is efficient but highly fragile, and in which finding solutions is very hard. In this latter case, failure at any point is likely to unleash very large cascading avalanches of propagating failure. This general phase transition has been confirmed in problems ranging from job shop problems to flow shop problems, to supply chain problems. An organization's flow shop could be on the wrong, fragile side of the phase transition. It is important to stress that, because there is a phase transition, the present invention can determine how much redundancy or added capacity needs to be added to be on the safe side of the phase transition, while addition of further capacity is of no real use. These operations management techniques have been described throughout this application above. [0533] Underlying these phase transitions is the concept of a technology graph, and its use as a mathematical analytic framework to examine, in one mathematical space, product and process design or operations - whether of a commercial project or of military logistics, or for infrastructures problems. The technology graph enables the present invention to identify robust means to achieve specific logistic or functional goals, yet degrade gracefully to neighboring goals or tasks if need be. [0534] For any given logistics, supply chain, or infrastructure problem, there are typically optimal pathways to carry out the desired task(s), and typically there are nearby pathways that are almost as good as the optimal pathway. The present invention, as discussed above, utilizes utilizing reinforcement learning, ant algorithms, and external dynamics—the latter invented by Dr. Per Bak—to find such families of nearby pathways to performance. The fact that these approaches find not only the optimal pathway, but also neighboring pathways indicates that they are complementary methodologies to find robust adaptive means of operations that will prevent avalanches of failures from propagating throughout the system, and will afford rapid recovery via neighboring adequate pathways. Techniques for finding optimal pathways using reinforcement learning and ant algorithms are described in detail above. [0535] A cousin of the above procedures is operational risk management, which was also described in detail above. Here, the aim is to understand the cascading coupled risk factors due to the non-linear couplings between different parts of a system. It is just such coupled risk factors, which are likely to give way successively in the avalanches of failure mentioned above. Thus, operational risk management is again a companion procedure to identify means to achieve robust reliable operational modes. [0536] In addition to the above, the present invention further includes means to analyze functionally coupled systems ranging from economic webs of complements and substitutes, to functionally coupled components in supply chains and elsewhere that should be applicable in infrastructure problems. The core ideas rest on descriptions of objects and operations in terms of their functional requirements, matching those requirements with other objects or operations, and building up functional wholes. Given the notion that neighboring objects or operations carry out similar tasks, it then becomes possible to utilize such models together with operational risk management to study the capacity for alternative members of a family of objects or operations to take over the role of a given object or operation that may have failed, hence to analyze the capacity of the functioning system to behave in a fault tolerant, robustly adaptive fashion. Techniques for synthesizing economic web are described in “A System and Method for the Synthesis of an Economic Web and the Identification of New Market Niches”, U.S. application Ser. No. 09/080,040, the contents of which are herein incorporated by reference. FIG. 25 shows a flow diagram of an exemplary method [0537] In summary, the present invention includes a number of novel related conceptual, algorithmic, mathematical, and software tools that can be combined to diagnose the capacity of a system to function in a fault tolerant way that is robustly adaptable, and also to design new, or modify existing systems to achieve such fault tolerant adaptive behavior. [0538]FIG. 26 discloses a representative computer system [0539] As shown in FIG. 26,representative computer system [0540] Storage devices Referenced by
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