A graphical method for simplifying Bayesian games

Thwaites, Peter; Smith, Jim Q.

doi:10.1016/j.ress.2017.05.012

Cited by 4 publications

(5 citation statements)

References 27 publications

(60 reference statements)

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“…Exploration of new representations is ongoing; one extension of the CEG could include the recently developed continuous time dynamic CEG, which is able to accommodate recurrent within the ABM structure and model holding times along the edges between events [17]. Further extensions of interest focus on CEGs which are able to represent the interactions of multi-agent systems players such as in [19], and agents looping through a CEG with changing probabilities over time depending on previous migration experience. Engaging with this research will provide many avenues of future research to build upon the work presented in this paper, enabling for more full and direct CEG-like representations of an even wider class of ABMs than those discussed above.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models

Strong¹,

McAlpine²,

Smith³

2021

Preprint

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Agent-Based Models (ABMs) are often used to model migration and are increasingly used to simulate individual migrant decision-making and unfolding events through a sequence of heuristic if-then rules. However, ABMs lack the methods to embed more principled strategies of performing inference to estimate and validate the models, both of which are of significant importance for real-world case studies. Chain Event Graphs (CEGs) can fill this need: they can be used to provide a Bayesian framework which represents an ABM accurately. Through the use of the CEG, we illustrate how to transform an elicited ABM into a Bayesian framework and outline the benefits of this approach.

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Section: Discussionmentioning

confidence: 99%

A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models

Strong¹,

McAlpine²,

Smith³

2021

Preprint

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“…A paper on the use of decision CEGs for multi-agent problems and games has already been written [23], where we show how the methods outlined in this paper can easily be adapted to the case where there is more than one decision maker. Under the assumptions that the structure of the game and the crude structure of each player's utility function are common knowledge, modelling for example an adversarial two-player game is straightforward.…”

Section: Discussionmentioning

confidence: 99%

“…Note that the description here and a modified version of Figure 1 (without probabilities) also appear in [23], where they constitute a brief introduction to the Decision CEG, before the narrative turns to discussion of how these graphs can be used for the representation and analysis of Games.…”

Section: Cegs and Decision Cegsmentioning

confidence: 99%

A new method for tackling asymmetric decision problems

Thwaites

Smith

2017

International Journal of Approximate Reasoning

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Chain Event Graphs are probabilistic graphical models designed especially for the analysis of discrete statistical problems which do not admit a natural product space structure. We show here how they can be used for decision analysis through designation of some nodes as decision nodes, and the addition of utilities. We provide a local propagation algorithm for finding an optimal decision strategy and maximising expected utility. We also compare CEGs with Influence diagrams, Valuation Networks, Sequential decision diagrams, Sequential influence diagrams and Decision circuits for the representation and analysis of asymmetric decision problems.

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“…The influence diagrams are often represented with the language of Bayesian Networks to represent conditional independence given a certain decision for the other players [14]- [17]. Similar formulations employ the same structure conditioning on events, as in Chain Event Graphs [18], [19].…”

Section: A Related Workmentioning

confidence: 99%

Factorization of Dynamic Games over Spatio-Temporal Resources

Zanardi

Bolognani

Censi

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at equilibrium to build a leaner game graph. When applicable, it yields fewer nodes, fewer players per game node, hence much faster solutions. While for the general case checking for independence of players requires to solve the game itself, we observe that for dynamic games in the robotic domain there exist exact heuristics based on the spatio-temporal occupancy of the individual players. We validate our findings in realistic autonomous driving scenarios showing that already for a 4player intersection we have a reduction of game nodes and solving time close to 99%.

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A graphical method for simplifying Bayesian games

Cited by 4 publications

References 27 publications

A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models

A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models

A new method for tackling asymmetric decision problems

Factorization of Dynamic Games over Spatio-Temporal Resources

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