Agent_Zero and Generative Social Science

Summary:

• What’s a sufficient explanation for social behaviors?

  • Typically: assume rational agents and compute Nash equilibrium solution

  • Proposal:

    • Look at cognitively plausible agents

    • Simulate them in an environment

    • Try to reproduce observed behaviors

• Agent Zero

  • Affective, deliberative and social modules

  • Agents interact and affect each other’s modules

  • Modules will be improved over time and the current goal is to set up an initial framework with all the key components

  • A formal, mathematical and computational alternative to the rational actor model

• Example:

  • Agent Zero’s environment interpreted as exposure to violence

  • 2D grid of sites through which agents move

  • Sites can “attack” agents and stimulate them to become afraid

  • Cause fear conditioning that lasts for a long time after the initial stimulus

  • Each agent’s solo disposition depends on their private history

  • Agents are also affected by dispositions of other agents

  • From these foundations we can create realistic behaviors

    • Agents A and B are attacked and become afraid

    • Agent C has never been attacked but us affected by the fear of A and B, and starts attacking others

• Framework applicable to many real-world situations

  • Free snake

  • Eat food

  • Join lynch mob

  • Refuse vaccines

  • Etc.

• Equations: minimal sufficient model that captures human behaviors

  • Solo disposition to act(t) = Affective(t) + Deliberative(t)

  • Total disposition to act(t) = Solo(t) + Sum of everyone else's Solo(t)*weight

    • Social contagion is based on sharing dispositions, not imitating actions

  • Act if Total disposition to act(t) > threshold

• Fear equation

  • Rescola-Wagner model

    • Fear acquisition

    • Fear extinction

    • Modeled based on the neuroscience of the amygdala circuit

      • Associative fear conditioning: a light alone doesn’t make you afraid but if you consistently shock a person while showing a light then after a while the light alone will cause fear

  • Fear contagion

    • Various neurological mechanisms (e.g. mirror neurons)

    • Experiments show that if a person watches another person undergoing fear conditioning training, they’ll become afraid when they see the light.

    • The same fear circuitry is routinely adapted to novel scenarios

      • Arab Face + 9/11 + fear

      • Doctor + Tuskegee + distrust

      • Financial asset + devaluation + panic

  • Fear is quickly learned and slowly forgotten over time

    • Exponential decay

    • Moving window average

  • Formula: Weight * Initial fear stimulus * exponential decay + memory (moving window average of stimuli)

    • Weight = the surprise level and salience level (how meaningful it is) of stimulus 

• Agent Zero exhibits some standard biases (biased by local and social influences)

  • There are many others

  • Anchoring bias

  • Loss aversion

  • …

• Parameters

  • Fear learning rate

  • Memory window

  • Threshold (equal across weights)

  • Must define stimulus landscape and initial conditions

• Network effects

  • Can create networks exogenous

  • Endogenous models

    • Connect agents that are similar in attribute space

    • Social influence proportional to agent similarity

• Example: Jury dynamics

  • Pre-trial: no communication among agents before trial

  • During trial: lawyers influence jurors but jurors still don’t communicate

  • Jury deliberations: jurors interact and can acquit or convict due to social interactions even if none of them would do this on their own

• Extension example: COVID fear

  • People are afraid due to COVID

  • They hide for a while, not afraid

  • Go back out, increase infections

  • Get afraid again, hide

  • Fear of disease can be combined with fear of vaccines, which can inflame disease and its fear.

  • Model produces multiple waves of infection, with secondary waves larger than the first one.

• Extension example: addiction

  • Early on in drug us: deliberative module dominates

  • Later on: affective module most important