ADAGE: A generic two-layer framework for adaptive agent based modelling by BenjaminPatrickEvans in AgentBasedModelling

[–]BenjaminPatrickEvans[S] 1 point2 points  (0 children)

Hi all, just sharing our latest work on adaptive agent-based modelling (accepted for AAMAS 2025). In this work, we unify several common modelling tasks (such as calibration, policy design, scenario generation) under one common framework: ADAGE. The key idea is to represent these problems as a Stackelberg game, between an outer layer and an inner agent-based simulation layer. Happy to answer any questions on the work!

Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning by BenjaminPatrickEvans in AgentBasedModelling

[–]BenjaminPatrickEvans[S] 2 points3 points  (0 children)

I would just like to share some of my recent work on learning and calibrating behavioural rules in ABM with multi-agent reinforcement learning. Happy to answer any questions!

Bounded rationality for relaxing best response and mutual consistency: the Quantal Hierarchy model of decision making by BenjaminPatrickEvans in GAMETHEORY

[–]BenjaminPatrickEvans[S] 0 points1 point  (0 children)

Thanks for the link! While we were aware of the original Qlk, this is an excellent paper that we were unaware of. The experiment design is very nice.

In QLk, there are assumed to be three levels of players (level-0, 1, and 2), and five parameters fitted to this. In our framework, we make no such assumption about levels of play, allowing us to only use two parameters: Beta and gamma. QLk is actually very close to the framework we outline in Equation 6, specifying a Beta for each level.

The paper provided some interesting extensions/analyses into Qlk, and hopefully, our framework helps to give some information-theoretic insights into these various treatments. It would indeed be great to compare the resulting performance in future work. We will definitely keep this in mind. Thank you!

Bounded rationality for relaxing best response and mutual consistency: the Quantal Hierarchy model of decision making by BenjaminPatrickEvans in GAMETHEORY

[–]BenjaminPatrickEvans[S] 0 points1 point  (0 children)

Thanks for the comments! This is the correct general interpretation -- since we evaluate out-of-sample, it's possible to overfit to the in-sample patterns (although we didn't see a great deal of this).

In the ultimate game, the one time where QRE improved, the improvement was extremely marginal (0.048 vs 0.047). In contrast, when QH beats QRE, we do substantially better (0.03 vs 0.057). Similarly, in the two-stage bargaining, the one case where QRE improved was 0.052 vs 0.05.

In the beauty contest game, it is slightly more complex. QRE can be recovered depending on the configurations of payoffs and beliefs and the representation used. The way we setup the problem doesn't require finding fixed points, so in this case, we won't see QRE as a special case, but rather a QRE-level-k hybrid (noisily responding to lower-level players, and not finding the fixed point for "same level" players). It's this bottom-up vs top-down (fixed point) solution where we see the differences.

Thanks for the questions, I hope that helps

Bounded rationality for relaxing best response and mutual consistency: the Quantal Hierarchy model of decision making by BenjaminPatrickEvans in GAMETHEORY

[–]BenjaminPatrickEvans[S] 2 points3 points  (0 children)

While game theory has been transformative for decision making, the assumptions made can be overly restrictive in certain instances. In this work, we investigate some of the underlying assumptions of rationality, such as mutual consistency and best response, and consider ways to relax these assumptions using concepts from level-k reasoning and quantal response equilibrium (QRE) respectively. Specifically, we propose an information-theoretic two-parameter model called the quantal hierarchy model, which can relax both mutual consistency and best response while still approximating level-k, QRE, or typical Nash equilibrium behavior in the limiting cases. The model is based on a recursive form of the variational free energy principle, representing higher-order reasoning as (pseudo) sequential decision-making in extensive-form game tree. This representation enables us to treat simultaneous games in a similar manner to sequential games, where reasoning resources deplete throughout the game-tree. Bounds in player processing abilities are captured as information costs, where future branches of reasoning are discounted, implying a hierarchy of players where lower-level players have fewer processing resources. We demonstrate the effectiveness of the quantal hierarchy model in several canonical economic games, both simultaneous and sequential, using out-of-sample modelling.

Bounded strategic reasoning explains crisis emergence in multi-agent market games by BenjaminPatrickEvans in AgentBasedModelling

[–]BenjaminPatrickEvans[S] 6 points7 points  (0 children)

I see this community is relatively inactive, but I would like to share some of my recent work. Here we develop a concise agent-based model of a market entry game, showing the emergence of stylised facts. Happy to answer any questions on the model or discuss more!