How do you test and refine your assumptions in a signaling game model?

1 Identify the key elements

The first step is to identify the key elements of your signaling game model, such as the players, the actions, the messages, the payoffs, and the information structure. You should also specify the assumptions you make about each element, such as how the players form beliefs, how the messages are interpreted, and how the payoffs are determined. You should be clear about the scope and purpose of your model, and how it relates to the empirical or theoretical context you are interested in.

2 Analyze the outcomes

The second step is to analyze the outcomes of your signaling game model, such as the possible equilibria, the strategies, the beliefs, and the welfare implications. You should use the tools of game theory, such as backward induction, best response, and Bayesian updating, to derive and compare the outcomes under different assumptions. You should also check the robustness and sensitivity of your results, such as how they change with different parameter values, information sets, or refinements.

3 Validate the predictions

The third step is to validate the predictions of your signaling game model, such as how they match or differ from the observed or expected behavior of the agents in the real world. You should use empirical methods, such as experiments, surveys, or data analysis, to test the validity and reliability of your predictions. You should also consider alternative explanations or hypotheses that could account for the same or different observations, and how you can rule them out or incorporate them into your model.

4 Evaluate the implications

The fourth step is to evaluate the implications of your signaling game model, such as how they inform or challenge the existing theories, policies, or practices in your field of interest. You should use normative methods, such as criteria, standards, or values, to assess the desirability or feasibility of your implications. You should also consider the limitations or caveats of your model, such as the assumptions you made, the parameters you chose, or the equilibria you selected, and how they affect the generalizability or applicability of your implications.

5 Communicate the findings

The fifth step is to communicate the findings of your signaling game model, such as how you present and explain your results, assumptions, predictions, and implications to your intended audience. You should use effective methods, such as graphs, tables, diagrams, or narratives, to convey your message clearly and concisely. You should also use appropriate methods, such as citations, references, or acknowledgments, to support your claims and credit your sources.

6 Revise the model

The sixth step is to revise your signaling game model, based on the feedback, criticism, or suggestions you receive from your audience, peers, or reviewers. You should use constructive methods, such as questions, comments, or recommendations, to identify and address the strengths and weaknesses of your model. You should also use iterative methods, such as trial and error, experimentation, or simulation, to improve and refine your model until you reach a satisfactory level of accuracy and relevance.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?