Error management theory is a theory of considerable scope and emerging influence. The theory claims that cognitive biases do not necessarily reflect flaws in evolutionary design, but that they may be best conceived as design features. Unfortunately, existing accounts are vague with respect to the key concept of bias. The result is that it is unclear that the cognitive biases that the theory seeks to defend are not simply a form of behavioral bias, in which case the theory reduces to a version of expected utility theory. We propose some clarifications and refinements of error management theory by emphasizing important distinctions between different forms of behavioral and cognitive bias. We also highlight a key assumption, that the capacity for Bayesian beliefs is subject to constraints. This assumption is necessary for
what we see as error management theory’s genuinely novel claim: that behavioral tendencies to avoid costly errors can rest on systematic departures from Bayesian beliefs, and that the latter can be adaptive insofar as they generate the former.
I study the effect of an increase in financial incentives for firms to hire disabled workers in the context of an employment quota. My results suggest that this increase had a positive impact on firms' demand for disabled workers.
We ask whether offering a menu of unemployment insurance contracts is welfare-improving in a heterogeneous population. We adopt a repeated moral hazard framework as in Shavell and Weiss(1979), supplemented by unobserved heterogeneity about agents’ job opportunities. Our main theoretical contribution is a quasi-recursive formulation of our adverse selection problem, including a geometric characterization of the state space. Our main economic result is that optimal contracts for ‘‘bad’’ searchers tend to be upward sloping due to an adverse selection effect. This is in contrast to the well-known optimal decreasing time profile of benefits in pure moral hazard environments that continue to be optimal for ‘‘good’’ searchers in our model.
The direct sale of emissions allowances by auction is an emerging characteristic of cap-and-trade programs. This study is motivated by the observation that all of the major implementations of cap-andtrade regulations for the control of air pollution have started with a generous allocation of allowances relative to recent emissions history, a situation we refer to as a “loose cap.” Typically more stringent reductions are achieved in subsequent years of a program. We use an experimental setting to investigate
the effects of a loose cap environment on a variety of auction types. We find all auction formats studied are efficient in allocating emissions allowances, but auction revenues tend to be lower relative to competitive benchmarks when the cap is loose. Regardless of whether the cap is tight or loose, the different auction formats tend to yield comparable revenues toward the end of a series of auctions. However, aggressive bidding behavior in initial discriminatory auctions yields higher revenues than in other auction formats, a difference that disappears as bidders learn to adjust their bids closer to the cutoff
that separates winning and losing bids.
We introduce a new combinatorial auction format based on a simple, transparent pricing mechanism tailored for the hierarchical package structure proposed by Rothkopf, Peke~c, and Harstad (1998) to avoid computational complexity. This combination provides the feedback necessary for bidders in multi-round auctions to discern winning bidding strategies for subsequent rounds and to coordinate responses to aggressive package bids. The resulting mechanism is compared to two leading alternatives in a series of laboratory experiments involving varying degrees of value synergies. Based on these 'wind tunnel' tests the FCC has decided to use hierarchical package bidding in the major upcoming 700MHz auction.
This paper reports laboratory experiments that evaluate the performance of a flexible packagebidding format developed by the FCC, in comparison with other combinatorial formats. In general, the interest of policy makers in combinatorial auctions is justified by the laboratory data; when value complementarities are present, package bidding yields improved performance. We find clear differences among the combinatorial auction formats, however, both in terms of efficiency and seller revenue. Notably, the combinatorial clock provides the highest revenue. The FCC’s flexible package bidding format performed worse than the alternatives, which is one of the main reasons why it was not implemented.
We experimentally study auctions versus grandfathering in the initial assignment of pollution permits that can be traded in a secondary spot market. Low and high emitters compete for permits in the auction, while permits are assigned for free under grandfathering. In theory, trading in the spot market should erase inefficiencies due to initial mis-allocations. In the experiment, high emitters exercise market power in the spot market and permit holdings under grandfathering remain skewed towards high emitters. Furthermore, the opportunity costs of “free” permits are fully “passed through.” In the auction, the majority of permits are won by low emitters, reducing the need for spot-market trading. Auctions generate higher consumer surplus and slightly lower product prices in the laboratory markets. Moreover, auctions eliminate the large “windfall profits” that are observed in the treatment with free, grandfathered permit allocations.
We combine survey data on friendship networks and individual characteristics with experimental observations from dictator games. Dictator offers are primarily explained by social distance - giving follows a simple inverse distance law. While student demographics play a minor role in explaining offer amounts, individual heterogeneity is important for network formation. In particular, we detect significant homophilous behavior - students connect to others similar to them. Moreover, the network data reveal a strong preference for cliques - students connect to those already close. The study is one of the first to identify network architecture with individual behavior in a strategic context.
