Extended Bayesian inference incorporating symmetry bias Abstract   We start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. We calculated the determination coefficients between assessments by participants in eight types of causal induction experiments and the estimated values using the proposed model. The mean coefficient of determination was 0.93. Thus, it can reproduce causal induction of human judgment with high accuracy. We further propose a humanlike Bayesian inference method to replace the conditional probability in Bayesian inference with the aforementioned causal induction model. In this method, two components coexist: the component of Bayesian inference, which updates the degree of confidence for each hypothesis, and the component of inverse Bayesian inference that modifies the model of each hypothesis. In other words, this method allows not only inference but also simultaneous learning. Our study demonstrates that the method addresses unsteady situations where the target of inference occasionally changes not only by making inferences based on knowledge (model) and observation data, but also by modifying the model itself.

On Amelioration Of Human Cognitive Biases In Binary Decision Making We study the behavior of cognitively biased humans in decision making under the binary hypothesis testing framework. Rationality of humans is modeled via prospect theory, which utilizes a value function and a weight function to characterize humansxe2x80x99 distorted perception of costs and probabilities. Following psychology studies which suggest that humans make decisions by choosing an alternative that yields the minimum expected costs based on received evidence, we show that a cognitively biased human employs an likelihood ratio test (LRT) in the prospect theoretic hypothesis testing framework. We further propose three approaches to ameliorate the side effects of cognitive biases and help humans make higher quality decisions. Simulations are presented to corroborate the theoretical results.