A Model of Belief Formation Based on Causality and Application to N-armed Bandit Problem

Abstract

keywords: causal induction, symmetry bias, mutual exclusivity bias, n-armed bandit problem, trade-off between exploration and exploitation SummaryThrough numbers of studies on the formation of equivalence relations and causal induction, it is known that human beings tend to consider conditional statements "if p then q" as biconditional statements "if and only if p then q": we call the tendency to perceive "if p then q" as "if q then p" the "symmetry bias". On the other hand, many studies on children's word learning have pointed out that children tend to expect each object has only one label. This is so-called the "mutual exclusivity bias". This bias implies that children infer "if not p then not q" from "if p then q".These biases logically mislead human beings. What is the merit of these illogical induction? In this paper we address this question. First, we clarify the relationship between causal induction and the symmetry bias or and the mutual exclusivity bias. Secondly, we propose a new model of causal induction. Thirdly, we construct an agent which makes illogical decision based on causality, and assess the agent's performance for the task called "n-armed bandit problem" in the field of reinforcement learning. In this problem, it is known that there is a "trade-off between exploration and exploitation". According to our simulation, the agent can resolve the trade-off and achieve quite better performance than an agent without these biases.

Implementation of Human Cognitive Bias on Naïve Bayes

“…Previous researches [6,7,8,9] have shown the capability of implementing human-cognition inspired model for machinelearning tasks. The well-used model called LS model flexibly adjusts the two biases ( and ) and has correlation to human-cognition [7].…”

“…It is known that human has illogical symmetric cognitive biases that induces from a proposition "if then " its converse "if then " and inverse "if ̅ then not ̅". The LS model quantitatively represents these tendencies [6]. Takahashi et.al.…”

“…Meanwhile, some studies [6,7,8,9] have indicated that the HumanCognitively inspired bias is able to enhance the prediction accuracy of machine learning algorithms. This HumanCognitively inspired model called "Loosely Symmetric (LS) model" introduced by Shinohara et.al.…”

Causal cognition and spam classifier

“…Human is known to have illogical symmetric cognitive biases that induce "if then "and "if not then not " from "if then ." The loosely symmetric Shinohara model quantitatively represents the tendencies, first proposed by Shinohara and later analyzed by one of the author [5]. Loosely symmetric flexibly adjusts these biases using universalities and have highly correlation with human causality [6].…”

The efficacy of symmetric cognitive biases in robotic motion learning

“…Though the inferences are logically invalid, the tendencies often have a beneficial effect on communication and decision-making in society and natural environment, since they underlie many heuristics and biases [11,12]. Shinohara's loosely symmetric (LS) model quantitatively models these two biases and their adjustment mechanism [13]. While it describes the human sense of causality the best [11], the agent that has LS model as the action value function exhibits fine performance in the classical two-armed bandit problems [14].…”