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

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

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

by Marko Rodriguez on Apr 27, 2010

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