Social Decision Making with Semantic Networks and Grammar-based Particle-Swarms
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Social Decision Making with Semantic Networks and Grammar-based Particle-Swarms

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Social decision support systems are able to aggregate the local perspectives of a diverse group of individuals into a global social decision. This paper presents a multi-relational network ontology ...

Social decision support systems are able to aggregate the local perspectives of a diverse group of individuals into a global social decision. This paper presents a multi-relational network ontology and grammar-based particle swarm algorithm capable of aggregating the decisions of millions of individuals. This framework supports a diverse problem space and a broad range of vote aggregation algorithms. These algorithms account for individual expertise and representation across different domains of the group problem space. Individuals are able to pose and categorize problems, generate potential solutions, choose trusted representatives, and vote for particular solutions. Ultimately, via a social decision making algorithm, the system aggregates all the individual votes into a single collective decision.

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Social Decision Making with Semantic Networks and Grammar-based Particle-Swarms Social Decision Making with Semantic Networks and Grammar-based Particle-Swarms Presentation Transcript

  • Social Decision Making with Semantic Networks and Grammar-based Particle-Swarms Marko A. Rodriguez Los Alamos National Laboratory http://cdms.lanl.gov
  • http://www.tagcrowd.com
  • Outline
    • General Vote System Model
    • Proposed Semantic Network Ontology
      • Tagging of individuals according to domains of trust and problems (issues) according domains
    • Grammar-based Particle Swarms
      • Rank solutions (options) by traversing the semantic network in a constrained manner.
    • Dynamically Distributed Democracy
    • Complete System Model
  • General Vote System Model Direct Democracy Majority Wins
  • General Vote System Model Social networks to support fluctuating levels of participation
  • Semantic Network Defined
    • Heterogeneous set of artifacts (nodes) and a heterogeneous set of relationships (edges).
    • An ontology abstractly defines the types of artifacts and set of possible relationships.
    • Requires “semantically-aware” graph algorithms for analysis.
  • Network Description
    • Social Network - Individuals connected to one another by domains of trust.
    • Decision Network - Individuals connected to the problems (issues) they raise/categorize and solutions (options) they propose.
    Humans Decisions
  • Social Network Description
    • Humans are related according to the domains in which they trust one another.
    • These domains can be top-down prescribed (taxonomy) or bottom-up defined (folksonomy).
    • Domains are related to one another by their subjective similarity or can be automatically related by various text analysis algorithms.
  • Social Network Ontology h_0 believes that h_2 will make a “good” decision. NOT USED - “warm up example”
  • Social Network Ontology h_0 believes that h_2 will make a “good” decision in the domain of economics , but not in the domain of politics . NOT USED - “warm up example” d_1 = economics d_0 = politics
  • Social Network Ontology h_0 believes that h_2 will make a “good” decision in the domain of d_1 ( economics ), but not in the domain of d_0 ( politics ). NOT USED - “warm up example”
  • Social Network Ontology h_0 believes that h_2 will make a “good” decision in the domain d_1 ( economics ) and furthermore, that d_0 ( politics ) is similar to d_1.
  • Decision Network Description
    • Humans raise problems (issues).
    • Humans categorize problems in particular domains.
    • Humans propose solutions to problems (options).
    • Humans vote on solutions.
  • Decision Network Ontology h_1 created problem p_0. h_0 proposed s_0 as a potential solution to p_0. h_2 categorized p_0 as in the domain d_0 and has voted on proposed solution s_2.
  • Grammar-Based Particles
    • The purpose of the particle swarm is to calculate a stationary probability distribution in a subset of the full decision making network.
      • eigenvector centrality, ?PageRank?, discrete form of constrained spreading activation.
    • The propagation of the particle is constrained by its grammar.
  • Grammar-Based Particles
    • Each particle has an abstract model of its allowed node and edge traversals (e.g. only take votedOn edges, or only go to Human nodes).
    • This can be represented as a finite state machine internal to the particle (aka. a grammar)
    • Each collective decision making algorithm is represented by a different grammar.
      • Direct Democracy and Dynamically Distributed Democracy (DDD).
        • (Representative Democracy, Dictatorship, Proxy Vote)
  • Grammar-Based Particles Particle Direct Democracy
  • Grammar-Based Particles Particle Dynamically Distributed Democracy
  • Grammar-Based Particles Dynamically Distributed Democracy Rodriguez, M.A., Steinbock, D.J., “Societal-Scale Decision Making with Social Networks”, NACSOS, 2004.
  • Complete System Model
  • Conclusion http://cdms.lanl.gov/ http://www.soe.ucsc.edu/~okram/ http://en.wikipedia.org/wiki/Dynamically_Distributed_Democracy