Switch to: References

Add citations

You must login to add citations.
  1. Studying strategies and types of players: experiments, logics and cognitive models.Sujata Ghosh & Rineke Verbrugge - 2018 - Synthese 195 (10):4265-4307.
    How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in terms of (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  • Strategic Reasoning: Building Cognitive Models from Logical Formulas.Sujata Ghosh, Ben Meijering & Rineke Verbrugge - 2014 - Journal of Logic, Language and Information 23 (1):1-29.
    This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects who (...)
    Download  
     
    Export citation  
     
    Bookmark   4 citations  
  • A Computational Learning Semantics for Inductive Empirical Knowledge.Kevin T. Kelly - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Cham, Switzerland: Springer International Publishing. pp. 289-337.
    This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations