Willpower and compromise effect

This paper provides a behavioral foundation for modeling willpower as a limited cognitive resource that bridges the standard utility maximization and Strotz models. Using the agent's ex ante preferences and ex post choices, we derive a representation that captures key behavioral traits of willpower-constrained decision making. We use the model to study the pricing problem of a profit-maximizing monopolist who faces consumers with limited willpower. We show that the optimal contract often consists of three alternatives and that the consumer's choices reflect a form of the "compromise effect," which is induced endogenously

Profit Sharing and Incentives

We model a firm as a team production process subject to moral hazard and derive the optimal profit sharing scheme between productive workers and outside investors together with incentive contracts based on noisy performance signals. More productive agents with noisier performance signals are more likely to receive shares which can explain why managers are motivated by shares, and law or consulting firms form partnerships. A firm that grows by opening branches is held almost entirely by outside investors when its output noise grows faster than the number of branches. Otherwise, insiders hold substantial amount of a large firm’s shares

The effects of rivalry on scientific progress under public vs private learning

We offer a model of scientific progress in which uncertainty resolves over time. We show that rivalry leads to less experimentation, extending results for preemption games to experimentation with uncertain outcomes. We compare experimentation duration and welfare when experimental outcomes are publicly versus privately observable. We show that public learning can generate more experimentation and higher welfare when uncertainty about the feasibility of a breakthrough is large; breakthroughs are rare even when they are feasible; and experiments produce results infrequently. Our results shed light on recent criticism of the science system

Making the Anscombe-Aumann approach to ambiguity suitable for descriptive applications

The Anscombe-Aumann (AA) model, originally introduced to give a normative basis to expected utility, is nowadays mostly used for another purpose: to analyze deviations from expected utility due to ambiguity (unknown probabilities). The AA model makes two ancillary assumptions that do not refer to ambiguity: expected utility for risk and backward induction. These assumptions, even if normatively appropriate, fail descriptively. This paper relaxes these ancillary assumptions to avoid the descriptive violations, while maintaining AA\xe2\x80\x99s convenient mixture operation. Thus, it becomes possible to test and apply all AA-based ambiguity theories descriptively while avoiding confounds due to violated ancillary assumptions. The resulting tests use only simple stimuli, avoiding noise due to complexity. We demonstrate the latter in a simple experiment where we find that three assumptions about ambiguity, commonly made in AA theories, are violated: reference independence, universal ambiguity aversion, and weak certainty independence. The second, theoretical, part of the paper accommodates the violations found for the first ambiguity theory in the AA model\xe2\x80\x94Schmeidler\xe2\x80\x99s CEU theory\xe2\x80\x94by introducing and axiomatizing a reference dependent generalization. That is, we extend the AA ambiguity model to prospect theory

Uncertainty and compound lotteries: calibration

This paper introduces a theoretical model of decision making in which preferences are defined on both Savage subjective acts and compound objective lotteries. Preferences are two-stage probabilistically sophisticated when the ranking of acts corresponds to the ranking of the respective compound lotteries induced by the acts through the decision maker’s subjective belief. This family of preferences includes various theoretical models proposed in the literature to accommodate non-neutral attitude towards ambiguity. The principle of calibration relates preferences over acts and compound objective lotteries, and provides a foundation for the tight empirical association between probabilistic sophistication and reduction of compound lotteries for all two-stage probabilistically sophisticated preferences

Dynamic Asset-Backed Security Design

Borrowers obtain liquidity by issuing securities backed by the current period payoff and resale price of a long-lived collateral asset, and they are privately informed about the payoff distribution. Asset price can be self-fulfilling: a higher asset price lowers adverse selection and allows borrowers to raise greater funding, which makes the asset more valuable, leading to multiple equilibria. Optimal security design eliminates multiple equilibria, improves welfare, and can be implemented as a repo contract. Persistent adverse selection lowers debt funding, generates volatility in asset prices and exacerbates credit crunches. The theory demonstrates the role of asset-backed securities on stability of market-based financial systems

Learning and complementarities in speculative attacks

We study a model where the aggregate trading of currency speculators reveals new information to the central bank and affects its policy decision. We show that the learning process gives rise to coordination motives among speculators leading to large currency attacks and introducing non-fundamental volatility into exchange rates and policy decisions. We show that the central bank can improve the ex ante effectiveness of its policy by committing to put a lower weight ex post on the information from the market, and that transparency may either increase or decrease the effectiveness of learning from the market, depending on how it is implemented