思維領導

We build on AGM belief revision (Alchourrón et al. (1985)) and propose a class of updating rules called pragmatic rules. Pragmatic updating applies to multiple priors and requires that the agent's posteriors be the subset of her priors under which the realized event occurs with probability 1, if such priors exist. We construct a propositional language based on qualitative probability and demonstrate the strong relation between belief updating rules and belief revision rules in this language. We show that an updating rule is consistent with AGM belief revision if and only if it is pragmatic. While maximum likelihood updating is pragmatic in general, full-Bayesian updating is not. We characterize maximum likelihood updating within the AGM framework, and show that full-Bayesian updating can be obtained by dropping one of AGM's postulates.

We introduce a learning model in which the decision maker does not know how recommendations are generated, called the contraction rule. We present behavioral postulates that characterize it. The contraction rule can be uniquely identified and reveals how the decision maker interprets and how much she trusts the recommendation. In a dynamic stationary setting, we show that the contraction rule is not dominated by completely following recommendations and is incompatible with a property called compliance with balanced recommendations. Following this negative result, we demonstrate that the contraction rule may generate and reinforce recency bias and disagreement.

Sharing economy platforms are pressed to rapidly grow user bases at the early stage by aggressively targeting potential users through competitive actions. Due to the volatile nature of the sharing economy and its disruption to industry norms, these platforms encounter legitimacy challenges that impede user base growth. This paper integrates competitive repertoire and institutional legitimacy theories to develop a research model that explains early-stage user base development in the sharing economy. We posit that the early-stage user base is associated with structural characteristics of the competitive repertoire, whose effects are moderated by a platform's socio-political legitimation efforts that address stakeholders’ regulatory and normative concerns. Using a comprehensive sample of 4644 monthly observations of 129 sharing economy platforms in China, we find that the volume of two context-specific competitive actions, offering economic incentives and staging high-visibility events, along with competitive repertoire complexity, are positively related to the platform's early-stage user base. We also identify a significant negative relationship between repertoire differentiation and user base. Direct relationships are moderated by socio-political legitimation, however, such that legitimation weakens the positive impact of context-specific action volume but enhances those of repertoire complexity and differentiation. Managerial and practical implications are discussed in light of the findings.