When faced with a decision, people collect information to help them decide. Though it may seem unnecessary, people often continue to search for information about alternatives after they have already chosen an option, even if this choice is irreversible (e.g., checking out other cars after just purchasing one). While previous post-decision search studies focused on “one-shot” decisions and highlighted its irrational aspects, here we explore the possible benefits of post-decision search in the long run. We use a simple search task in which participants repeatedly decide whether to select the current alternative or continue to search for a better alternative. In a preliminary study we find that participants indeed conduct post-decision search even in unique environments, where information about forgone options cannot be used in future choices. In the main studies exposure to post-decision information was manipulated directly in unique environments, and was found to lead to better performance. The source of the observed improvement was further investigated with an explicit strategy elicitation methodology. We find that following exposure to post-decision information, people collect more data before generating thresholds. Thus, although post-decision search in unique environments might appear redundant, our results suggest it can help decision makers to modify their strategy and improve their future choices.

As nudge interventions have become more popular, academic research is developing that assesses to what extent these interventions are effective. My paper contributes to this stream of research: collating and synthesising evidence on the effectiveness of nudge interventions that aim to increase consumer search and switching in retail financial markets. Following a systematic search strategy, I identify 35 relevant papers, including qualitative studies, laboratory experiments, field experiments and ex post analyses, covering a range of retail financial products and different types of nudges. The review results in two main contributions. First, it demonstrates that different study designs serve different purposes in evidence accumulation. Second, based on over 400 estimates extracted from these papers, it establishes that the currently available evidence shows that nudges increase consumer search and switching in retail financial markets by 2–3 percentage points on average. Structural nudges that change the choice architecture more profoundly have a higher impact on search and switching than nudges that provide, simplify or highlight information. While nudge interventions may be efficient on a cost–benefit basis and can lead to a large increase in relative terms (e.g. doubling switching rates from 1% to 2%), regulators cannot expect them to significantly alter consumer behaviour.

We use a labor search model with heterogenous households and firms to study the efficacy of a wage subsidy during a pandemic, relative to enhancing unemployment benefits. A large proportion of the economy is forced to shut down, and firms in that sector choose whether to lay off workers or keep them on payroll. A wage subsidy encourages firms to keep workers on payroll, which speeds up labor market recovery after the pandemic ends. However, a wage subsidy can be costlier than enhancing unemployment benefits. If the shutdown is long or profit margins are low, then a wage subsidy is preferable and vice versa. The optimal mixture of policies includes a wage subsidy that covers 90$\%$ of the first $200/week of earnings and expands unemployment benefits to cover all salary up to $275/week. Low-income workers, as well as those in less productive jobs, benefit the most from a wage subsidy.

This paper proposes and evaluates swarming mechanisms of patrolling unmanned aerial vehicles (UAVs) that can collectively search a region for intruding UAVs. The main contributions include the development of multi-objective searching strategies and investigation of the required sensor configurations for the patrolling UAVs. Numerical results reveal that it is sometimes better to search through a region with a single swarm rather than multiple swarms deployed over sub-regions. Moreover, a large communication range does not necessarily improve search performances, and the patrolling swarm must have a speed close to the speed of the intruding UAVs to maximize the search performances.

We introduce business-to-business (B2B) relationships into an otherwise standard model to revisit two aspects of price dynamics in a unified analysis. On one side, the pass-through of cost shocks to prices is empirically incomplete. On the other side, the literature contains conjectures that long-term relationships may reduce the allocative role of price changes. After a partial equilibrium analysis of these aspects, we consider the general equilibrium effects. The formation of B2B relationships implies that the trade of intermediate goods depends on search, bargaining, and the adjustment along the intensive margin as opposed to the extensive margin. We find that, when this adjustment is costly, retailers have a relatively high bargaining power, and mismatch shocks are possible, the model can account for the second moments of the US producer price index and other variables. In this case, although its allocative role is low, the intermediate goods price affects the allocation of goods through the search externalities and is sufficiently volatile. The analysis includes several sensitivity tests and comparisons.

We construct a microfounded model of money with Gul–Pesendorfer preferences. In each period, agents are tempted to spend all their money by the end of the period, and they suffer from the forgone utility that could have been obtained by adopting the tempting choice. We find that the Friedman rule may not be optimal. A positive nominal interest rate improves welfare because it reduces the real money balances and renders the temptation less attractive. The welfare gained by deviating from the rule is equivalent to 0.67% of consumption.

We show that, in the large-firm search model, employment may decrease even when the level of the introduced minimum wage lies below the equilibrium wage of the laissez-faire economy. Wages also decrease in the presence of the minimum wage. The argument is based on multiple equilibria and the idea that, in a large-firm context, the representative firm may choose to overemploy workers in order to renegotiate lower wages.

Models of endogenous growth have not been able to account for the variety of empirically observed distributional properties of the returns to innovation, in part, because of the limitations necessarily imposed on competition to cope with increasing returns to scale. Exponential growth, fat tails, Pareto–Levy distributed upper tails, and upper value outliers, are associated with increasing returns to scale and innovation. At the same time, properties such as bifurcated research investment strategies, bimodal returns to innovation, and Laplace distributed firm growth rates are products of competition. We build an agent-based model of endogenous technical change in which heterogeneous investments in patented knowledge and increasing returns to scale emerge these distributional properties within a competitive market. The combination of heterogeneous agents, costly information, and patents allow for a competitive landscape to persist amidst increasing returns. The ability of model to foster a coexistence of competition and increasing returns underlies the observed distributional properties.

This paper presents a model of development that is driven by matching between talents and technologies. Differences in productivity across countries are amplified by three dimensions of talent utilization: the range of talents utilized, the density of a specific talent utilized, and the average match quality in the economy. In our model, higher productivity increases the number of technologies available, enhancing the opportunities for individuals to match their talents to specific technologies and increasing the returns to search. More intensive search further contributes to talent utilization.

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.