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We provide a result on prospect theory decision makers who are naïve about the time inconsistency induced by probability weighting. If a market offers a sufficiently rich set of investment strategies, investors postpone their trading decisions indefinitely due to a strong preference for skewness. We conclude that probability weighting in combination with naïveté leads to unrealistic predictions for a wide range of dynamic setups. (JEL D81, G02, G11)

A (partially naive) quasihyperbolic discounter repeatedly chooses whether to complete a task. Her net benefits of task completion are drawn independently between periods from a timeinvariant distribution. We show that the probability of completing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish nonidentifiability by proving that for any timepreference parameters and any dataset with such (weakly increasing) taskcompletion probabilities, there exists a stationary payoff distribution that rationalizes the agent's behavior if she is either sophisticated or fully naive. Additionally, we provide sharp partial identification for the case of observable continuation values.

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly provided that the agent's prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical driftdiffusion model (DDM), where the agent knows the utility difference between the choices.

We develop an axiomatic theory of information acquisition that captures the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and generating a signal with probability half costs half its original cost. Together with Blackwell monotonicity and a continuity condition, these axioms determine the cost of a signal up to a vector of parameters. These parameters have a clear economic interpretation and determine the difficulty of distinguishing states.

We study a generalization of the classical monopoly insurance problem under adverse selection (see Stiglitz 1977) where we allow for a random distribution of losses, possibly correlated with the agent's risk parameter that is private information. Our model explains patterns of observed customer behavior and predicts insurance contracts most often observed in practice: these consist of menus of several deductiblepremium pairs or menus of insurance with coverage limitsâ€“premium pairs. A main departure from the classical insurance literature is obtained here by endowing the agents with riskaverse preferences that can be represented by a dual utility functional (Yaari 1987).
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