Research Plan

The overarching goal is to explore the dynamics of a hypothetical decentralized decision-making process through the lens of agent-based modeling. By the end of the semester I would like to have a model which includes a spontaneously-generating social graph and a dynamic, scalar decision-making process.

Fundamental Challenge:

• How to measure the results?
• Baysian Regret measured against abstract ‘Law String’ – the current version
• Something else – it would be nice if the model could posit a series of problems which the ‘society’ must solve – similar to a Genetic Algorithm with the complication that the agents have selfish motives as well as altruistic ones

A Proposal:

The “generic interactive unit” (agent) consists of three functions – INPUT (sensing), PROCESSING (cognition), OUTPUT (action), in a dynamic (often parallel, often self-referential) loop. Emergence is the phenomenon whereby a collection of individual units operating in parallel produce predictable ‘meta-behaviors’. This model is endlessly extensible. c.f. Conversations paper.

Week 3

• PROGRAMMING : Build a statistical version of the current model
• Intro page which allows user to set parameters:
• number of agents
• number of voters per proposal
• normalized or linear law preference strings
• granularity of preference strings
• Checkboxes to turn on and off:
• whether the proposer is included as a voter
• passion function – how deeply each voter cares about each preference – affects BR calculations and participation likelihood
• noise (reflecting agents’ misunderstanding of the law or its effects on them)
• random law (for comparison)
• Let the program runs each combination 1000 times and summarize the sata to  average BR, StDev of BR, volatility (frequency of law changes per 1000 iterations)
• Observe if there are patterns apparent in the various configurations
• STUDY:
• Growing Artificial Societies
• The Stag Hunt and the Evolution of Social Structure

Week 4

• PROGRAMMING : Create a spacial version in which agents can move, interact and propose local ‘laws’.
• Allow user to set parameter:
• Size of ‘field’
• STUDY:

Week 5

• PROGRAMMING :  Create a spontaneously self-generating social graph that evolves a power-law distribution
• Introduce agent variability:
• Trustworthiness
• Gregariousness
• STUDY: TBD

Week 6

• PROGRAMMING : Create a system for innovation and creativity – a ‘genetic’ legal system that accounts for novelty
• STUDY: TBD

Week 7

• PROGRAMMING : Copy the system of trade from Growing Artificial Societies; then add to that a system for setting the rules of exchange which is itself evolutionary
• STUDY: TBD

Week 8

• PROGRAMMING: Explore the possibility of a self-referential model – one that alters its own rules as it progresses
• STUDY: TBD