We consider a common investment project that is vulnerable to a self-fulfilling coordination failure and hence is strategically risky. Based on their private information, agents -who have heterogeneous investment incentives -form expectations or "sentiments" about the project's outcome. We find that the sum of these sentiments is constant across different strategy profiles and it is independent of the distribution of incentives. As a result, we can think of sentiment as a scarce resource divided up among the different payoff types. Applying this finding, we show that agents who benefit little from the project's success have a large impact on the coordination process. The agents with small benefits invest only if their sentiment towards the project is large per unit investment cost. As the average sentiment is constant, a subsidy decreasing the investment costs of these agents will "free up" a large amount of sentiment, provoking a large impact on the whole economy. Intuitively, these agents, insensitive to the project's outcome and hence to the actions of others, are influential because they modify their equilibrium behavior only if the others change theirs substantially.JEL classification: C7, D8, O12.

We solve a general class of dynamic rational inattention problems in which an agent repeatedly acquires costly information about an evolving state and selects actions. The solution resembles the choice rule in a dynamic logit model, but it is biased toward an optimal default rule that is independent of the realized state. The model provides the same fit to choice data as dynamic logit, but, because of the bias, yields different counterfactual predictions. We apply the general solution to the study of (i) the status quo bias; (ii) inertia in actions leading to lagged adjustments to shocks; and (iii) the tradeoff between accuracy and delay in decision‐making.

When an agent chooses between prospects, noise in information processing generates an effect akin to the winner's curse. Statistically unbiased perception systematically overvalues the chosen action because it fails to account for the possibility that noise is responsible for making the preferred action appear to be optimal. The optimal perception pattern exhibits a key feature of prospect theory, namely, overweighting of small probability events (and corresponding underweighting of high probability events). This bias arises to correct for the winner's curse effect. (JEL D11, D81, D82, D83)There is considerable evidence that human perception of reality is noisy and biased.1 While randomness can be understood as a technological limitation of human cognition, systematic behavioral biases, such as those documented in the psychological experiments of Kahneman and Tversky (1979), are more puzzling. Since there is no obvious reason why natural or cultural evolution could not remove these biases, their prevalence suggests that they serve a purpose. This paper argues that perception biases arise as a second-best solution when some noise in information processing is unavoidable. In particular, we show that overweighting of small probability events optimally mitigates errors due to randomness. Our model also provides a framework for conceptualizing errors in decision making, allowing us to consider, for example, whether overweighting of small probabilities is a mistake or an optimal heuristic. Finally, our results demonstrate 1 McFadden (1999, p. 96) summarizes the experimental evidence as follows: "Humans fail to retrieve and process information consistently… These failures may be fundamental, the result of the way human memory is wired. I conclude that perception-rationality fails, and that the failures are systematic, persistent, pervasive, and large in magnitude."

Although the Vlasov equation is used as a good approximation for a sufficiently large N , Braun and Hepp have showed that the time evolution of the one particle distribution function of a N particle classical Hamiltonian system with long range interactions satisfies the Vlasov equation in the limit of infinite N . Here we rederive this result using a different approach allowing a discussion of the role of inter-particle correlations on the system dynamics. Otherwise for finite N collisional corrections must be introduced. This has allowed the a quite comprehensive study of the Quasi Stationary States (QSS) but many aspects of the physical interpretations of these states remain unclear. In this paper a proper definition of timescale for long time evolution is discussed and several numerical results are presented, for different values of N . Previous reports indicates that the lifetimes of the QSS scale as N 1.7 or even the system properties scales with exp(N ). However, preliminary results presented here shows indicates that time scale goes as N 2 for a different type of initial condition. We also discuss how the form of the inter-particle potential determines the convergence of the N -particle dynamics to the Vlasov equation. The results are obtained in the context of following models: the Hamiltonian Mean Field, the Self Gravitating Ring Model, and a 2-D Systems of Gravitating Particles. We have also provided information of the validity of the Vlasov equation for finite N , i. e. how the dynamics converges to the mean-field (Vlasov) description as N increases and how inter-particle correlations arise.