How Does Biased Information Sampling Lead to Biases in Judgment: A Review from a Biased Sampling Perspective

Abstract

Individual judgment often systematically deviates from the optimal beliefs and choices hypothesized by rational-agent models, leading to cognitive illusions and biases in judgment. Researchers have observed an enormous number of biased beliefs and behaviors in laboratory and field studies since 1970s, such as confirmatory bias, overconfidence, false correlation, base-rate neglect, herd behavior, etc. This biased way of judgment reveals the limitations of rational-agent models and has been an important concern in management & decision science, behavioral economics, psychology, and sociology. Pertaining studies concerning biased judgment in psychology attempt to model and understand the nature of these biases by un-riddling the mental processes of judgment, resulting in various influential perspectives. In particular, individuals’ internal motivational constraints and cognitive deficits are widely concerned by researchers and are regarded as the main mechanisms for the formation of biased judgment. In terms of motivational perspective, researchers attribute selective exposure, information distortion and confirmatory bias to the motivation of cognitive coherence, and ascribe biases in social judgments, like obedience and herd behavior, to the motivation of affiliation. In terms of cognitive perspective, researchers attribute biases in judgment to heuristic search and attribution substitution induced by individuals’ limited cognitive resources in the process of information processing. Previous studies have shed substantial light on individuals’ motivation and cognition, but little on individuals’ interactions with the environment and the biasing impact of these interactions. However, as claimed by Simon, bounded rationality is the result of interplay of the mind and environment. In order to understand human behavior, one first has to understand the environment in which the behaviors take place. In 2000, Fiedler introduced the concept of sampling” in an important paper published in Psychological Review, emphasizing the impact of distribution of information in the environment on the acquisition process of decision samples（information sampling）and judgment. Many biases in judgment can be attributed to biased sampling, namely decision-makers consciously or unconsciously take the biased and incomplete information samples they draw from the population as the representation of the environment. As the environment component of Simon’s bounded rationality, the biased sampling perspective plays a constructive role in providing alternative accounts for the existing decision biases. In the last decade, the biased sampling perspective has received much attention. Inspired by the perspective of information sampling, researchers discover a new bias effect description-experience gap”. In addition, the idea of information sampling is integrated into the decision-making models in management and economics, e.g. query theory, decision field theory, leaky-competing accumulator model and decision by sampling. However, although a variety of researchers have attempted to explore the formation mechanism of judgment from the perspective of biased sampling, relatively little attention has been given to this perspective in China. In response to this situation, in current research, an alternative account for the mechanisms underlying biased judgment is presented, which highlights individuals’ information sampling from the environment in the spirit of the biased sampling approach launched by the influential figures like Fiedler, Juslin, Denrell, Le Mens, Hertwig, etc. This paper proceeds as follows: by introducing the conception of sampling proposed by Fiedler, the specific psychological and environmental mechanisms are induced, and resulting biases in judgment are elaborated. Then, the description-experience gap, a typical bias resulting from biased information sampling, is comprehensively delineated, followed by several enlightening sampling-based decision models. Based on a literature review, this paper appeals to the academia for future scholarly endeavors from the perspective of biased information sampling, especially the investigation of the characteristics of the environment which are more likely to lead to biased information sampling. It adds to the theoretical foundations of biased judgment, and provides practical implications for understanding and correcting the bounded rationality of human decision-making.