Question for Joshua Epstein on Generative and Inverse Generative Social Science: Agent_Zero, the Rational Actor, and Beyond

[xqrue-49]

Generative and Inverse Generative Social Science: Agent_Zero, the Rational Actor, and Beyond. Professor Epstein will discuss (1) the epistemology of generative social science (2) Agent_Zero as a formal cognitively plausible alternative to the Rational Actor, and (3) the use of AI, and Genetic Programming in particular, to evolve families of generative agent architectures computationally.

Reading List:

• Epstein, J.M., 2023. Inverse generative social science: Backward to the future. Journal of artificial societies and social simulation: JASSS, 26(2), p.9.

[dario-marino]

Hello, I don't understand the Friedman Gambit Declined answer. Why if the agents are represented by a model we cannot say that they behave as if they were reducible to the model?

[saniazeb8]

Thank you for sharing interesting work. I would be intrigued to know how do we differ rational agent from an intelligent agent and dynamic learning behavior of agents?

[Yunrui11]

Thank you for sharing! When inverse generative methods produce several Agent Zero variants that all match the same macro‑pattern, what kinds of empirical evidence or tests do you find most useful for deciding which internal mechanisms are actually operative in real populations?

[PaulaTepkham]

Thank you very much. This is my first time learning about inverse generative social science (iGSS), and I find the idea of inverting the traditional agent-based modeling approach incredibly fascinating.

Regarding Figure 4 in the paper, you mentioned that the model improves not gradually but in distinct jumps. What explains the sudden performance improvements between Stage 1 and Stage 2, and again between Stage 3 and Stage 4? Are there specific reasons why these behavioural modules (like normalisation or cohesion) cause such sharp drops in target loss?

[yifand1023]

Thanks for sharing! My question is that how Agent_Zero challenge or complement traditional models of the Rational Actor in explaining emergent social phenomena—especially in cases where emotion or social influence overrides individual utility maximization?

[Kevin2330]

Thanks for sharing.

In Agent_Zero, affective and social modules supplement cognition—how might these modules evolve differently across cultural contexts, and could inverse generative methods help uncover those variations?

[zcyou018]

Thank you for sharing! Given the epistemological emphasis on generative explanation—especially in the context of Agent_Zero's triple-process architecture (emotion, bounded deliberation, and social influence)—how do you envision integrating neurobiological findings or cognitive science constraints into the evolution of agent primitives themselves, not just their combinations?

[zcyou018]

Hello, I don't understand the Friedman Gambit Declined answer. Why if the agents are represented by a model we cannot say that they behave as if they were reducible to the model?

I think the issue is that saying agents behave "as if" they’re optimizing doesn’t explain how their behavior actually arises. It fits outcomes, but doesn’t reveal the underlying cognitive or social processes.

[wendixue]

Thank you for this insightful work. I’m especially intrigued by the potential of inverse generative methods to surface multiple micro-level pathways leading to the same observed macro-patterns. From a sociological perspective, could this be used to examine how different identity performances—especially among gender or sexual minorities—emerge under similar structural conditions? In other words, could iGSS help theorize the plurality of queer social formations as generative equivalents rather than anomalies?

[ralphluo11]

In your 2023 paper, you emphasize the potential of “inverse generative social science” and propose using AI and genetic programming to explore families of agent architectures beyond the Rational Actor and Agent_Zero. Given that inverse methods can lead to multiple plausible generative models, how should researchers evaluate or choose between these competing explanations? Should priority be given to parsimony, predictive power, empirical fit, or explanatory richness—and how might these criteria differ across domains like public health vs. political behavior?

[yifan-lyf]

How does Agent_Zero provide a more cognitively plausible alternative to the traditional Rational Actor model in generative social science?

[ZheruiLU]

Thanks for sharing! Your paper observes that the absence of a meaningful distance metric in “rule space” blocks ordinary local-sensitivity analysis. At the same time, inverse generative methods often return several distinct agent architectures that fit the target macro-pattern equally well. How, then, should we assess robustness and choose among these candidate models—especially when we want to draw policy-relevant conclusions? Could information-theoretic similarity measures, behavioural-experiment validation, or ensemble forecasting fill the gap, or does iGSS require a wholly new robustness paradigm?

[Jessieliao2001]

Thanks for sharing! How could inverse generative social science (iGSS)—which evolves micro‐agents to reproduce macro patterns—reshape our understanding of economic growth dynamics compared to traditional representative‐agent models?

[Jessieliao2001]

Thank you for sharing! Given the epistemological emphasis on generative explanation—especially in the context of Agent_Zero's triple-process architecture (emotion, bounded deliberation, and social influence)—how do you envision integrating neurobiological findings or cognitive science constraints into the evolution of agent primitives themselves, not just their combinations?

You could bake cognitive and neurobiological realism right into your “alphabet” of agent primitives by constraining their form and fitness with empirically-grounded measures. For example, impose working-memory limits drawn from human RT and error-rate studies on your deliberation primitives, or use reinforcement-learning parameters calibrated to dopamine-signaling dynamics for your emotion module. Likewise, borrow connectivity motifs from resting-state fMRI (e.g. small-world clusters with hub nodes) to shape your social-influence operators. During the inverse‐evolution phase, you’d then score candidate primitives not only on how well they reproduce macro patterns, but also on how closely their parameter distributions and interaction topologies match neurocognitive benchmarks. This way the “atoms” of your evolved agents remain tethered to real brain and behavior data, rather than floating as purely abstract rules.

[oliang2000]

Very interesting paper! By prioritizing explanation as the central objective, the model integrates heterogeneity in parameters, yet it does not take into account variations in the rules governing individual agents. I am curious about the potential structure of a model that encompasses agents with distinct operational rules, and how you intend to juxtapose the outcomes of such a model with empirical observations?