title: Opinion dynamics with confirmation bias.
abstract: BACKGROUND
  Confirmation bias is the tendency to acquire or evaluate new information in a way that is consistent with one's preexisting beliefs. It is omnipresent in psychology, economics, and even scientific practices. Prior theoretical research of this phenomenon has mainly focused on its economic implications possibly missing its potential connections with broader notions of cognitive science.
  
  
  RESULTS
  We formulate a (non-Bayesian) model for revising subjective probabilistic opinion of a confirmationally-biased agent in the light of a persuasive opinion. The revision rule ensures that the agent does not react to persuasion that is either far from his current opinion or coincides with it. We demonstrate that the model accounts for the basic phenomenology of the social judgment theory, and allows to study various phenomena such as cognitive dissonance and boomerang effect. The model also displays the order of presentation effect-when consecutively exposed to two opinions, the preference is given to the last opinion (recency) or the first opinion (primacy) -and relates recency to confirmation bias. Finally, we study the model in the case of repeated persuasion and analyze its convergence properties.
  
  
  CONCLUSIONS
  The standard Bayesian approach to probabilistic opinion revision is inadequate for describing the observed phenomenology of persuasion process. The simple non-Bayesian model proposed here does agree with this phenomenology and is capable of reproducing a spectrum of effects observed in psychology: primacy-recency phenomenon, boomerang effect and cognitive dissonance. We point out several limitations of the model that should motivate its future development.
authors.affiliation: Department of Theoretical Physics, Yerevan Physics Institute, Yerevan, Armenia.
authors.forename: Armen E
authors.initials: AE
authors.lastname: Allahverdyan
authors.affiliation: USC Information Sciences Institute, Marina del Rey, California, United States of America.
authors.forename: Aram
authors.initials: A
authors.lastname: Galstyan
mesh_terms: D000465:Algorithms; D003071:Cognition; D006801:Humans; D008960:Models, Psychological