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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 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
Marko A. Rodriguez
Los Alamos National Laboratory
http://cdms.lanl.gov
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
Direct Democracy
Majority Wins
General Vote System Model
General Vote System ModelSocial 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” decisionin the domain of economics, but not in the domainof politics.
NOT USED - “warm up example”
d_1 = economicsd_0 = politics
Social Network Ontology
h_0 believes that h_2 will make a “good” decisionin the domain of d_1 (economics), but not in thedomain of d_0 (politics).
NOT USED - “warm up example”
Social Network Ontology
h_0 believes that h_2 will make a “good” decisionin 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 asa potential solution to p_0. h_2 categorized p_0 as inthe 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 ParticlesParticle
Direct Democracy
Grammar-Based ParticlesParticle
Dynamically Distributed Democracy
Grammar-Based ParticlesDynamically 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
