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Schneider, Mark; Deck, Cary; Shor, Mikhael; Besedeš, Tibor; Sarangi, Sudipta
doi: 10.1287/deca.2018.0379pmid: N/A
This paper investigates decision quality in large choice sets across several choice architectures in three studies. In the first controlled experiment, we manipulate two features of a choice architecture—the response mode (for ranking alternatives) and presentation mode (for presenting alternatives). Our design objectively ranks all 16 choice options in each choice set and makes it possible to observe decision quality directly, independent of attitudes toward risk. We find joint presentation outperforms separate presentation and that choice response modes outperform “happiness ratings,” which outperform hypothetical monetary valuations. We also apply classic welfare criteria to assess the performance of the architectures. Our key finding is that low cognitive reflection subjects (as measured by the cognitive reflection test) perform better given a large choice set than given smaller sets collectively containing the same alternatives. This illustrates a basic tradeoff confronting choice architectures: for a fixed choice set, fewer options improve decision quality within that set but require architectures to elicit multiple responses, increasing opportunities for errors. One follow-up study demonstrates the robustness of the response mode result in a comparison using the tournament presentation mode. A second follow-up study reveals that the impact of incentivizing monetary valuations depends on cognitive reflection.
doi: 10.1287/deca.2018.0375pmid: N/A
The prevention of malicious attacks on secured areas, such as airports and government installations, is a major concern everywhere as terror organizations increase their global reach. Typically, traffic in and out of such protected areas happens through well-defined gates. An attacker who wants to penetrate the area has to do it through one of the gates, and the defender who protects the area tries to prevent it by inspecting the incoming traffic. The trade-off faced by the defender is that increased inspections adversely affect the vast majority of innocent people passing through the gates by causing longer queues and discomfort. We combine game theory considerations with queueing theory analysis to address such situations. We formulate and analytically solve a defender–attacker game that takes place at multiple gates of a secured area. The gates are modeled as M/M/N queueing systems, where the “N”s are chosen strategically by the defender and may vary among the gates. Our analysis shows scenarios where even a limited number of guards is sufficient to deter would-be attackers and there is no need to assign additional guards even if they are available at no further cost.
Yolmeh, Abdolmajid; Baykal-Gürsoy, Melike
doi: 10.1287/deca.2018.0377pmid: N/A
Protecting infrastructures and their users against terrorist attacks involves making both strategic and operational decisions in an organization’s hierarchy. Although usually analyzed separately, these decisions influence each other. To study the combined effect of strategic and operational decisions, we present a game-theoretic, two-stage model between a defender and an attacker involving multiple target sites. In the first stage, the defender (attacker) makes a strategic decision of allocating investment resources to target sites to improve the defense (attack) capabilities. We consider two cases for investments in the first stage: (1) unconstrained and (2) budget-constrained models. The investment allocations for each target site determine its detection probability. In the second stage, the players make operational decisions of which target site to defend or to attack. We distinguish between two games that arise in the second stage: the maximal damage game and the infiltration/harassment game. We prove that the solution to each game under budget constraints is unique. In fact, when the second-stage game is of the infiltration/harassment type, the invest–defend game has a unique closed-form solution that is very intuitive. The results reveal that an increase in defense investments at a target site decreases the probability of both defending and attacking that target. However, an increase in attack investments increases the probability of both defending and attacking that target. Similarly, an increase in the defender’s (attacker’s) investment efficiency leads to a decrease (increase) in investments of both the defender and the attacker. Finally, the model is applied to real data to obtain the equilibrium investment and defense strategies. The results from real data demonstrate that the attacker’s penalty from a failed attack is an important factor in determining the defender’s optimal distribution of investments and defense probabilities. The defender’s second-stage defense decisions complement the first-stage investment decisions; that is, among target sites that receive little or zero investment, the most important one is covered with a relatively high defense probability in the second stage. Moreover, as the attacker’s budget increases, the defense investments shift from less important sites to more important ones.
Hadlock, Christopher C.; Bickel, J. Eric
doi: 10.1287/deca.2018.0376pmid: N/A
Johnson quantile-parameterized distributions (J-QPDs) are parameterized by any symmetric percentile triplet (SPT) (e.g., the 10th–50th–90th) and support bounds. J-QPDs are smooth, highly flexible, and amenable to Monte Carlo simulation via inverse transform sampling. However, semibounded J-QPDs are limited to lognormal tails. In this paper we generalize the kernel distribution of J-QPD beyond the standard normal, generating new fat-tailed distribution systems that are more flexible than J-QPD. We also show how to augment the SPT/bound parameters with a tail parameter, lending separate control over the distribution body and tail. We then present advantages of our new generalized system over existing systems in the contexts of both expert elicitation and fitting to empirical data.
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