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This paper deals with a basic issue: How does one approach the problem of designing the \"right\" objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of designing objectives that account for the natural tension between efficiency and fairness in the context of a framework that captures a number of resource allocation problems of interest to managers. More precisely, we consider a rich family of objectives that have been well studied in the literature for their fairness properties. We deal with the problem of selecting the appropriate objective from this family. We characterize the trade-off achieved between efficiency and fairness as one selects different objectives and develop several concrete managerial prescriptions for the selection problem based on this trade-off. Finally, we demonstrate the value of our framework in a case study that considers air traffic management. This paper was accepted by Yossi Aviv, operations management.

This paper constructs an efficient, budget-balanced, Bayesian incentive-compatible mechanism for a general dynamic environment with quasilinear payoffs in which agents observe private information and decisions are made over countably many periods. First, under the assumption of \"private values\" (other agents' private information does not directly affect an agent's payoffs), we construct an efficient, ex post incentive-compatible mechanism, which is not budget-balanced. Second, under the assumption of \"independent types\" (the distribution of each agent's private information is not directly affected by other agents' private information), we show how the budget can be balanced without compromising agents' incentives. Finally, we show that the mechanism can be made self-enforcing when agents are sufficiently patient and the induced stochastic process over types is an ergodic finite Markov chain.

When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost—benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost—benefit comparison can be explained as a stochastic accumulation of cost—benefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.

We present simple one-shot distribution experiments comparing the relative importance of efficiency concerns, maximin preferences, and inequality aversion, as well as the relative performance of the fairness theories by Gary E Bolton and Axel Ockenfels and by Ernst Fehr and Klaus M. Schmidt. While the Fehr-Schmidt theory performs better in a direct comparison, this appears to be due to being in line with maximin preferences. More importantly, we find that a combination of efficiency concerns, maximin preferences, and selfishness can rationalize most of the data while the Bolton-Ockenfels and Fehr-Schmidt theories are unable to explain important patterns.

Data envelopment analysis (DEA), as originally proposed, is a methodology for evaluating the relative efficiencies of a set of homogeneous decision-making units (DMUs) in the sense that each uses the same input and output measures (in varying amounts from one DMU to another). In some situations, however, the assumption of homogeneity among DMUs may not apply. As an example, consider the case where the DMUs are plants in the same industry that may not all produce the same products. Evaluating efficiencies in the absence of homogeneity gives rise to the issue of how to fairly compare a DMU to other units, some of which may not be exactly in the same \"business.\" A related problem, and one that has been examined extensively in the literature, is the missing data problem; a DMU produces a certain output, but its value is not known. One approach taken to address this problem is to \"create\" a value for the missing output (e.g., substituting zero, or by taking the average of known values), and use it to fill in the gaps. In the present setting, however, the issue isn't that the data for the output is missing for certain DMUs, but rather that the output isn't produced. We argue herein that if a DMU has chosen not to produce a certain output, or for any reason cannot produce that output, and therefore does not put the resources in place to do so, then it would be inappropriate to artificially assign that DMU a zero value or some \"average\" value for the nonexistent factor. Specifically, the desire is to fairly evaluate a DMU for what it does, rather than penalize or credit it for what it doesn't do. In the current paper we present DEA-based models for evaluating the relative efficiencies of a set of DMUs where the requirement of homogeneity is relaxed. We then use these models to examine the efficiencies of a set of manufacturing plants.

Cross-efficiency evaluation has been widely used for identifying the most efficient decision making unit (DMU) or ranking DMUs in data envelopment analysis (DEA). Most existing approaches for cross-efficiency evaluation are focused on how to determine input and output weights uniquely, but pay little attention to the aggregation process of cross-efficiencies and simply aggregate them equally without considering their relative importance. This paper focuses on aggregating cross-efficiencies by taking into consideration their relative importance and proposes three alternative approaches to determining the relative importance weights for cross-efficiency aggregation. Numerical examples are examined to show the importance and necessity of the use of relative importance weights for cross-efficiency aggregation and the most efficient DMU can be significantly affected by taking into consideration the relative importance weights of cross-efficiencies.

For production systems composed of parallel processes, the system efficiency will more properly represent the aggregate performance of the component processes if the operation of each process is taken into consideration. Several approaches have been proposed for measuring the efficiency of parallel production systems. By requiring the same factor to have the same virtual multiplier, the proposed method is able to calculate the system and process efficiencies at the same time. Moreover, the former can be decomposed into a weighted average of the latter. A system is efficient only if all of its component processes are efficient. Chemistry departments in the UK are used as an example, where teaching and research are two major functions of the department. The relationship between the system and process efficiencies which holds for the case of constant returns to scale can be extended to the case of variable returns to scale.

The household sector is one of the most energy-intensive sectors in Europe, and thus a focal point for reducing greenhouse gas emissions associated with energy consumption. Energy efficiency is considered a key measure to reduce household energy consumption, but several factors could lead to an underinvestment in energy efficiency. This is the so-called energy efficiency gap or paradox. The factors in question are grouped under market failures (including informational failures), behavioural failures and other factors. Various policies can be used to address these failures and promote the adoption of energy-efficient technologies, including energy standards and codes, economic incentives and information instruments. This paper reviews the empirical evidence to date on energy efficiency policies and discusses their effectiveness. On the one hand, command and control instruments seem to be effective policies, but they have to overcome several barriers. In the case of price instruments, subsidies and taxes do not seem to be effective while rebates present mixed results as they sometimes are effective and in other cases, they could present significant shortcomings. Finally, the effectiveness of informational policies is not always ensured as they depend on the country, sector and product category. Information feedback tools also seem to be effective as they work as a constant reminder of energy-efficient behaviour. Some limitations of energy efficiency policies are also identified, such as the difficulties of implementing codes and standards given that a minimum level need to be achieved, differences in the effectiveness of rebate programmes and non-conclusive results in regard to the effectiveness of monetary energy efficiency labels.

This paper compares centralized and decentralized coordination when managers are privately informed and communicate strategically. We consider a multi-divisional organization in which decisions must be adapted to local conditions but also coordinated with each other. Information about local conditions is dispersed and held by self-interested division managers who communicate via cheap talk. The only available formal mechanism is the allocation of decision rights. We show that a higher need for coordination improves horizontal communication but worsens vertical communication. As a result, decentralization can dominate centralization even when coordination is extremely important relative to adaptation.

We conducted online field experiments in large real-world social networks in order to decompose prosocial giving into three components: (1) baseline altruism toward randomly selected strangers, (2) directed altruism that favors friends over random strangers, and (3) giving motivated by the prospect of future interaction. Directed altruism increases giving to friends by 52% relative to random strangers, whereas future interaction effects increase giving by an additional 24% when giving is socially efficient. This finding suggests that future interaction affects giving through a repeated game mechanism where agents can be rewarded for granting efficiency-enhancing favors. We also find that subjects with higher baseline altruism have friends with higher baseline altruism.