Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Introduction
- Part II Concepts and Techniques
- Part III Reflections and Elaborations
- Part IV Applications
- 10 Coherence as Constraint Satisfaction
- 11 Analogy as Structure Mapping
- 12 Communication as Bayesian Inference
- Appendix A Mathematical Background
- Appendix B List of Computational Problems
- Appendix C Compendium of Complexity Results
- References
- Index
12 - Communication as Bayesian Inference
from Part IV - Applications
Published online by Cambridge University Press: 18 April 2019
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Introduction
- Part II Concepts and Techniques
- Part III Reflections and Elaborations
- Part IV Applications
- 10 Coherence as Constraint Satisfaction
- 11 Analogy as Structure Mapping
- 12 Communication as Bayesian Inference
- Appendix A Mathematical Background
- Appendix B List of Computational Problems
- Appendix C Compendium of Complexity Results
- References
- Index
Summary
In this chapter, we consider a computational-level theory of communication as Bayesian inference. We again illustrate the use of classical complexity analysis to assess the theory’s intractability. In addition, we show that parameterized complexity analysis can converge with theoretically informed intuitions about possible sources of this intractability, in this case based on the Gricean Maxims in pragmatic theories of communication.
- Type
- Chapter
- Information
- Cognition and IntractabilityA Guide to Classical and Parameterized Complexity Analysis, pp. 246 - 257Publisher: Cambridge University PressPrint publication year: 2019