Knowledge is not manageable, as if it were data or information. Consequently, Knowledge Management (KM) must address activities that utilise and create knowledge, instead of addressing knowledge by itself. With regard to this issue, we elaborated on a sociotechnical approach of KM within the...

Knowledge is not manageable as if it was a data or information. Consequently, Knowledge Management (KM) must address activities that utilize and create knowledge more than knowledge by itself. With regard to this issue, we elaborated a sociotechnical approach of Knowledge Management within the...

State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the...

This paper develops a general framework to study how misinterpreting information impacts learning. We consider sequential social learning and passive individual learning settings in which individuals observe signals and the actions of predecessors. Individuals have incorrect, or misspecified...

This paper investigates the learnability of an equilibrium where agents formulate their forecasts under adaptive learning with heterogeneously misspecified econometric models; the equilibrium is called a Heterogeneous Misspecification Equilibrium (HME). The paper finds that the learnability...

This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long-run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then...

Under-reaction is a robust response to model misspecification rewarded by financial markets, rather than an “irrational” attitude that leads to extinction. I show that under-reacting rules guarantee predictions as accurate as Bayes' in well-specified learning problems and beat Bayes' in many...

We establish convergence of beliefs and actions in a class of one-dimensional learning settings in which the agent's model is misspecified, she chooses actions endogenously, and the actions affect how she misinterprets information. Our stochastic-approximation-based methods rely on two crucial...

We study learning and information acquisition by a Bayesian agent whose prior belief is misspecified in the sense that it assigns probability 0 to the true state of the world. At each instant, the agent takes an action and observes the corresponding payoff, which is the sum of a fixed but...

We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one prior distribution over a set of parameters and provide necessary and sufficient condition for ambiguity to fade away because of learning. Our condition applies to most learning environments: iid...