Download - 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

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

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”


h_0 believes that h_2 will make a “good” decisionin the domain of economics, but not in the domainof politics.


d_1 = economicsd_0 = politics


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).



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