Members of a supply chain often make profit comparisons. A retailer exhibits peer‐induced fairness concerns when his own profit is behind that of a peer retailer interacting with the same supplier. ...In addition, a retailer exhibits distributional fairness when his supplier's share of total profit is larger than his own. While existing research focuses exclusively on distributional fairness concerns, this study investigates how both types of fairness might interact and influence economic outcomes in a supply chain. We consider a one‐supplier and two‐retailer supply chain setting, and we show that (i) in the presence of distributional fairness alone, the wholesale price offer is lower than the standard wholesale price offer; (ii) in the presence of both types of fairness, the second wholesale price is higher than the first wholesale price; and (iii) in the presence of both types of fairness, the second retailer makes a lower profit and has a lower share of the total supply chain profit than the first retailer. We run controlled experiments with subjects motivated by substantial monetary incentives and show that subject behaviors are consistent with the model predictions. Structural estimation on the data suggests that peer‐induced fairness is more salient than distributional fairness.
The replicability of some scientific findings has recently been called into question. To contribute data about replicability in economics, we replicated 18 studies published in the American Economic ...Review and the Quarterly Journal of Economics between 2011 and 2014. All of these replications followed predefined analysis plans that were made publicly available beforehand, and they all have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We found a significant effect in the same direction as in the original study for 11 replications (61%); on average, the replicated effect size is 66% of the original. The replicability rate varies between 67% and 78% for four additional replicability indicators, including a prediction market measure of peer beliefs.
Peer-Induced Fairness in Games Ho, Teck-Hua; Su, Xuanming
The American economic review,
12/2009, Letnik:
99, Številka:
5
Journal Article
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People exhibit peer-induced fairness concerns when they look to their peers as a reference to evaluate their endowments. We analyze two independent ultimatum games played sequentially by a leader and ...two followers. With peer-induced fairness, the second follower is averse to receiving less than the first follower. Using laboratory experimental data, we estimate that peer-induced fairness between followers is two times stronger than distributional fairness between leader and follower. Allowing for heterogeneity, we find that 50 percent of subjects are fairness-minded. We discuss how peer-induced fairness might limit price discrimination, account for low variability in CEO compensation, and explain pattern bargaining.
Backward induction is a widely accepted principle for predicting behavior in sequential games. In the classic example of the "centipede game," however, players frequently violate this principle. An ...alternative is a "dynamic level-
k
" model, where players choose a rule from a rule hierarchy. The rule hierarchy is iteratively defined such that the level-
k
rule is a best response to the level-
(k-1)
rule, and the level-
∞
rule corresponds to backward induction. Players choose rules based on their best guesses of others' rules and use historical plays to improve their guesses. The model captures two systematic violations of backward induction in centipede games, limited induction and repetition unraveling. Because the dynamic level-
k
model always converges to backward induction over repetition, the former can be considered to be a tracing procedure for the latter. We also examine the generalizability of the dynamic level-
k
model by applying it to explain systematic violations of backward induction in sequential bargaining games. We show that the same model is capable of capturing these violations in two separate bargaining experiments.
This paper was accepted by Peter Wakker, decision analysis.
The format of pricing contracts varies substantially across business contexts, a major variable being whether a contract imposes a fixed fee payment. This paper examines how the use of the fixed fee ...in pricing contracts affects market outcomes of a manufacturer-retailer channel. Standard economic theories predict that channel efficiency increases with the introduction of the fixed fee and is invariant to its framing. We conduct a laboratory experiment to test these predictions. Surprisingly, the introduction of the fixed fee fails to increase channel efficiency. Moreover, the framing of the fixed fee does make a difference: an opaque frame as quantity discounts achieves higher channel efficiency than a salient frame as a two-part tariff, although these two contractual formats are theoretically equivalent.
To account for these anomalies, we generalize the standard economic model by allowing the retailer's utilities to be reference dependent so that the up-front fixed fee payment is perceived as a loss and the subsequent retail profits as a gain. We embed this reference-dependent utility function in a quantal response equilibrium framework where the retailer is allowed to make decision mistakes due to computational complexity. The key prediction of this behavioral model is that channel efficiency decreases with loss aversion for sufficiently Nash-rational retailers. Consistent with this prediction, the estimated loss-aversion coefficient is 1.37 in the two-part tariff condition, significantly higher than 1.27 in the quantity discount condition. At the same time, loss aversion dominates contract complexity in explaining the data. Lastly, we conduct a follow-up experiment to confirm the central role of loss aversion as a behavioral driver. In one condition, the retailer becomes less loss averse when we temporally compress the fixed fee payment and the realization of retail profits, which supports the loss aversion theory. In the other condition, the retailer's contract acceptance rate does not decline when we reward the manufacturer a higher cash payment for each experimental point earned, which rules out the competing hypothesis that the retailer rejects contract offers due to fairness concerns.
Being able to replicate scientific findings is crucial for scientific progress
. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science ...between 2010 and 2015
. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
Operations management (OM) researchers have traditionally focused on developing normative mathematical models that prescribe what managers and firms should do. Recently, there has been increased ...interest in understanding what managers and firms actually do and the factors that drive these decisions. To advance this understanding, empirical investigation using causal inference models is critical. However, in many contexts, the ability to obtain causal inferences is fraught with the challenges of endogeneity and selection bias. This paper describes five empirical tools that have been widely used in economics to address these challenges and how they can be adopted by OM researchers. We also present an example that illustrates how the various attributes of big data—variety, velocity, and volume—can be useful in addressing the endogeneity bias.
The online appendix is available at
https://doi.org/10.1287/msom.2017.0659
.
In ‘experience‐weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities ...according to some rule (e.g., logit). A key feature is a parameter δ that weights the strength of hypothetical reinforcement of strategies that were not chosen according to the payoff they would have yielded, relative to reinforcement of chosen strategies according to received payoffs. The other key features are two discount rates, φ and ρ, which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, and hybridizes their key elements. When δ= 0 and ρ= 0, cumulative choice reinforcement results. When δ= 1 and ρ=φ, levels of reinforcement of strategies are exactly the same as expected payoffs given weighted fictitious play beliefs. Using three sets of experimental data, parameter estimates of the model were calibrated on part of the data and used to predict a holdout sample. Estimates of δ are generally around .50, φ around .8 − 1, and ρ varies from 0 to φ. Reinforcement and belief‐learning special cases are generally rejected in favor of EWA, though belief models do better in some constant‐sum games. EWA is able to combine the best features of previous approaches, allowing attractions to begin and grow flexibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief‐based models implicitly do.
We propose a behavioral theory to predict actual ordering behavior in multilocation inventory systems. The theory rests on a well-known stylized fact of human behavior: people's preferences are ...reference dependent. We incorporate reference dependence into the newsvendor framework by assuming that there are psychological costs of leftovers and stockouts. We also hypothesize that the psychological aversion to leftovers is greater than the disutility for stockouts. We then experimentally test the proposed theory in both the centralized and decentralized inventory structures using subjects motivated by substantial financial incentives. Consistent with the proposed theory, actual orders exhibit the so-called "pull-to-center" bias and the degree of bias is greater in the high-profit margin than in the low-profit margin condition. These systematic biases are shown to eliminate the risk-pooling benefit when the demands across store locations are strongly correlated. Because the proposed model nests the standard inventory and ex post inventory error minimization theories as special cases, one can systematically evaluate the predictive power of each alternative using the generalized likelihood principle. We structurally estimate all three theories using the experimental data, and the estimation results strongly suggest that the proposed behavioral theory captures actual orders and profits better. We also conduct two experiments to validate the behavioral model by manipulating the relative salience of the psychological costs of leftovers versus that of stockouts to alleviate the pull-to-center bias.
The training process of a machine learning (ML) model may be subject to adversarial attacks from an attacker who attempts to undermine the test performance of the ML model by perturbing the training ...minibatches, and thus needs to be protected by a defender. Such a problem setting is referred to as training-time adversarial ML. We formulate it as a two-player game and propose a principled Recursive Reasoning-based Training-Time adversarial ML (R2T2) framework to model this game. R2T2 models the reasoning process between the attacker and the defender and captures their bounded reasoning capabilities (due to bounded computational resources) through the recursive reasoning formalism. In particular, we associate a deeper level of recursive reasoning with the use of a higher-order gradient to derive the attack (defense) strategy, which naturally improves its performance while requiring greater computational resources. Interestingly, our R2T2 framework encompasses a variety of existing adversarial ML methods which correspond to attackers (defenders) with different recursive reasoning capabilities. We show how an R2T2 attacker (defender) can utilize our proposed nested projected gradient descent-based method to approximate the optimal attack (defense) strategy at an arbitrary level of reasoning. R2T2 can empirically achieve state-of-the-art attack and defense performances on benchmark image datasets.
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