MULTIFACTORIAL OPTIMIZATION SYSTEM AND METHOD

• US 20070087756A1
• Filed: 08/29/2006
• Published: 04/19/2007
• Est. Priority Date: 10/04/2005
• Status: Active Grant

• Current Assignee: Meadwestvaco (WestRock Company)
• Original Assignee: Meadwestvaco (WestRock Company)
• Inventors: Hoffberg, Steven
• Granted Patent: US 8,874,477 B2
• Time in Patent Office: Days
• US Class Current: 455/450
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/103 Workflow collaboration or project management
• CPC Class Codes: G06Q 20/065 using e-cash
• CPC Class Codes: G06Q 20/22 Payment schemes or models
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 50/188 Electronic negotiation
• CPC Class Codes: H04W 16/28 using beam steering
• CPC Class Codes: H04W 64/003 locating network equipment
• CPC Class Codes: H04W 72/541 using the level of interference
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A method for providing unequal allocation of rights among agents while operating according to fair principles, comprising assigning a hierarchal rank to each agent; providing a synthetic economic value to a first set of agents at the a high level of the hierarchy; allocating portions of the synthetic economic value by the first set of agents to a second set of agents at respectively different hierarchal rank than the first set of agents; and conducting an auction amongst agents using the synthetic economic value as the currency. A method for allocation among agents, comprising assigning a wealth generation function for generating future wealth to each of a plurality of agents, communicating subjective market information between agents, and transferring wealth generated by the secure wealth generation function between agents in consideration of a market transaction. The method may further comprise the step of transferring at least a portion of the wealth generation function between agents.

Claims

1. A method for fairly allocating use rights among agents for at least one of at least one communications channel in a network and at least one computational resource in a computer system, comprising:

(a) determining a use demand function for a plurality of consumer agents;

(b) determining a use supply function for a plurality of supply agents,

(c) providing at least one economic agent, said economic agent providing at least one function to other agents selected from the group consisting of loaning an economic resource, borrowing an economic resource, insuring a use, guaranteeing a use, arbitraging use rights, buying use options, selling use options, buying use futures, selling use futures;

wherein a single agent can fulfill one or more role selected from the roles of supply agent, consumer agent, and economic agent, and wherein each respective role may have independent valuation;

(d) permitting the consumer agents, supply agents and economic agents to transact in self interest to achieve a market economy optimization.

2. The method according to claim 1, wherein the use comprises a right to access at least one communications channel in a communication network.

3. The method according to claim 1, wherein the use comprises a right to access at least one communications channel in an ad hoc communication network.

4. The method according to claim 1, wherein the use comprises a right to access at least one communications channel in a multihop ad hoc communication network.

5. The method according to claim 1, wherein the use comprises a right to access at least one computational resource in a computer system.

6. The method according to claim 1, wherein the use comprises a right to access at least one processor core in a computer system.

7. The method according to claim 1, wherein the use comprises a right to access at least communication channel in a computer system.

8. The method according to claim 1, wherein the use comprises a right to access at least one memory location in a computer system.

9. The method according to claim 1, wherein the use comprises a right to access at least one peripheral device in a computer system.

10. The method according to claim 1, wherein the use comprises a right to access at least one functional processor element in a computer system.

11. The method according to claim 1, wherein the use comprises a right to access at least one functional processor element in a computer system.

12. The method according to claim 1, wherein an agent issues a loan for economic consideration.

13. The method according to claim 1, wherein an agent issues a loan for consideration in kind.

14. The method according to claim 1, wherein an economic agent borrows an economic resource for economic consideration.

15. The method according to claim 1, wherein an economic agent borrows an economic resource for consideration in kind.

16. The method according to claim 1, wherein an economic agent insures a use of a resource.

17. The method according to claim 1, wherein an economic agent guarantees a payment for use of a resource.

18. The method according to claim 1, wherein an agent buys and sells the same resource in a set of transactions.

19. The method according to claim 1, wherein an agent sells an option for purchase of a resource.

20. The method according to claim 1, wherein an agent buys an option for purchase of a resource.

21. The method according to claim 1, wherein an agent sells a future contract for transferring a use at a later time.

22. The method according to claim 1, wherein an agent buys a future contract for acquiring a use at a later time.

23. The method according to claim 1, wherein at least two agents are assigned a hierarchal rank, and wherein one of the at least two agents has a higher rank than another of the at least two agents, further comprising the step of delegating an economic right from the higher ranked agent to the lower ranked agent.

24. The method according to claim 1, wherein at least two agents are assigned a hierarchal rank, and wherein one of the at least two agents has a higher rank than another of the at least two agents, further comprising the step of allocating an economic right held by lower ranked agent on behalf of the higher ranked agent.

25. The method according to claim 1, wherein a plurality of agents are each respectively assigned a rank, and wherein a lower ranked agent delegates rights to the higher ranked agent, separately from the market economy optimization.

26. The method according to claim 1, wherein the agents transact using a virtual currency.

27. The method according to claim 1, wherein the agents transact using a virtual currency, each agent having currency generation function.

28. The method according to claim 1, wherein the agents transact using a virtual currency, each agent having currency generation function and a currency expiration function.

29. The method according to claim 1, wherein the agents transact using a cryptographically authenticated virtual currency.

30. The method according to claim 1, wherein the agents transact using a virtual currency, and different agents have respectively different currency generation functions and currency expiration functions.

31. The method according to claim 1, wherein said market economy optimization comprises an auction.

32. The method according to claim 1, wherein said market economy optimization comprises a multifactorial auction.

34. The method according to claim 1, wherein said auction allocates communication bandwidth within a communication network.

35. The method according to claim 1, wherein said market economy optimization operates according to a Vickrey-Clark-Groves combinatorial auction framework.

36. The method according to claim 1, wherein the agents transact using a virtual currency, and a value of the virtual currency being location-dependent.

37. The method according to claim 1, wherein the agents transact using a virtual currency produced by an agent using a generation function, wherein an agent can transfer at least a portion of the generation function to another agent.

38. The method according to claim 1, wherein the market economy optimization compensates at least one consumer agent for deferring to another consumer agent.

39. A wireless mesh network node, comprising:

(a) a first antenna for communication through a first communication channel, the first antenna having a directional pattern with a useful aperture having a directional vector;

(b) a geolocation input;

(c) a second antenna for communicating through a second communication channel; and

(d) a controller, for receiving information through the second antenna defining in conjunction with the geolocation input a directional vector for the first communication channel, and for controlling a communication through the first antenna in accordance with the directional vector.

40. The wireless mesh network node according to claim 39, wherein the first antenna comprises an electronically steerable antenna.

41. The wireless mesh network node according to claim 39, wherein the first antenna comprises a phased array antenna.

42. The wireless mesh network node according to claim 39, wherein the first antenna comprises a mechanically steerable antenna.

43. The wireless mesh network node according to claim 39, wherein the second antenna is an omnidirectional antenna.

44. The wireless mesh network node according to claim 39, wherein the first antenna and second antenna operate in different bands.

45. The wireless mesh network node according to claim 39, wherein the first communication channel and the second communication channel have respectively different protocols.

46. The wireless mesh network node according to claim 39, wherein the first communication channel has a respectively shorter range than the second communication channel.

47. The wireless mesh network node according to claim 39, wherein the first communication channel is adapted to support a multihop communication protocol.

48. A system for analyzing a data set having a magnitude within a first set of dimensions representing a time domain, comprising:

a memory for storing the data set, a processor for formulating a prediction of a magnitude at a time, based on antecedent data, performing at least one data transform between a time domain and time-frequency hybrid domain, and predicting a magnitude of at least one future datum of the data set.

49. The system according to claim 48, wherein the processor applies a threshold function to the time-frequency hybrid domain representation of the data.

50. The system according to claim 48, wherein the processor applies a distance-selective function to the time-frequency hybrid domain representation of the data.

51. The system according to claim 48, wherein the processor applies a low-pass spatial filtering function to the time-frequency hybrid domain representation of the data.

52. The system according to claim 48, wherein the processor applies a high-pass spatial filtering function to the time-frequency hybrid domain representation of the data.

53. The system according to claim 48, wherein the processor applies a band-pass spatial filtering function to the time-frequency hybrid domain representation of the data.

54. The system according to claim 48, wherein the processor applies a noise-filtering function to the time-frequency hybrid domain representation of the data.

55. The system according to claim 48, wherein at least one data point within the data set are associated with a probabilistic distribution, and wherein the processor performs the at least one transform between the time domain and data a time-frequency hybrid domain on the probabilistic distribution.

56. The system according to claim 48, wherein a plurality of data points within the data set are associated with probabilistic distributions expressed as probability density functions, and wherein the processor performs the at least one transform of the probability density functions between the time domain and data a time-frequency hybrid domain.

57. The system according to claim 48, wherein the processor performs at least one inverse transform between the time domain and the time-frequency hybrid domain.

58. The system according to claim 48, wherein at least one of the data set and the time-frequency hybrid domain represented data is a sparse data set.

59. The system according to claim 48, wherein the processor processes at least one of the time domain data set and the time-frequency hybrid domain represented data set to increase its sparsity.

60. The system according to claim 48, wherein the data set comprises a risk parameter and a reliability parameter, said risk parameter representing a predicted cost or value associated with a datum and said reliability parameter representing a statistical probability distribution of the representation of the cost or value.

61. The system according to claim 60, wherein at least the risk parameter is subjective.

62. The system according to claim 60, wherein the reliability parameter is adaptive.

63. The system according to claim 60, wherein said risk parameter is at least ambiguous, having at least two inferred causalities, and wherein each inferred causality has an associated reliability parameter.

64. A human computer interface system for presenting information, comprising:

(a) a memory for storing data representing at least one event, the at least one event being associated with a risk parameter and a reliability parameter, the risk parameter representing a predicted severity associated with the event and the reliability parameter representing a statistical probability distribution of the representation of the severity;

(b) a processor for analyzing the at least one event based on both its associated risk parameter and reliability parameter to determine a relevance; and

(c) an output for presenting an identification of the at least one event to a user dependent on its relevance.

65. The human computer interface system according to claim 64, wherein the memory stores data representing a plurality of events, the processor generates a value for each of the plurality of events, and the output ranks at least a subset of the plurality of events in dependence on an associated value.

66. The human computer interface system according to claim 64, wherein the relevance is further dependent on at least one subjective consideration.

67. The human computer interface system according to claim 66, further comprising a memory for storing a set of subjective data associated with a user.

68. The human computer interface system according to claim 64, wherein the relevance of an event is dependent on a risk-tolerance of a user.

69. The human computer interface system according to claim 64, wherein the severity of the event is expressed as an economic parameter having an associated cost or value.

70. The human computer interface system according to claim 64, wherein the processor transforms the data between a time domain and a hybrid time-frequency domain, wherein said relevance is analyzed, at least in part, based on the hybrid time-frequency domain data representation.

71. The human computer interface system according to claim 64, wherein the event relates to at least one of traffic conditions associated with one or more regions of a roadway and weather.