Browsing by Subject "Fairness"
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Item Open Access Balance in resource allocation problems: a changing reference approach(Bilkent University, 2018-05) Erkan, HaleFairness has become one of the primary concerns in resource allocation problems, especially in settings which are associated with public welfare. Using a pure e ciency maximizing approach may not be applicable while distributing resources among entities, hence we propose a novel structure for integrating balance into the allocation process. In the proposed approach, balance is de ned and measured as the deviation from a reference distribution determined by the decision maker. We acknowledge that what is considered balanced by the decision maker might change with respect to the level of total output distributed. To provide an allocation policy that is in line with this changing structure of balance, we allow the decision maker to change her reference distribution depending on the total amount of output (bene t). We illustrate our approach using a project portfolio selection problem. We formulate a mixed integer mathematical programming model for the problem with maximizing e ciency and minimizing imbalance objectives. The bi-objective model is initially solved with the epsilon constraint method. However for larger problem instances this approach fails to nd solutions within reasonable time limits. Hence we implement metaheuristic algorithms and report on their performance. As an alternative solution method, an interactive algorithm is presented and used to nd the most preferred solution of the decision maker. The proposed resource allocation model provides important insights to decision makers regarding the tradeo between e ciency and fairness, and provides a useful tool to incorporate speci c balance concerns into the problem.Item Open Access Balance in resource allocation problems: a changing reference approach(Springer, 2020) Karsu, Özlem; Erkan, HaleFairness is one of the primary concerns in resource allocation problems, especially in settings which are associated with public welfare. Using a total benefit-maximizing approach may not be applicable while distributing resources among entities, and hence we propose a novel structure for integrating balance into the allocation process. In the proposed approach, imbalance is defined and measured as the deviation from a reference distribution determined by the decision-maker. What is considered balanced by the decision-maker might change with respect to the level of total output distributed. To provide an allocation policy that is in line with this changing structure of balance, we allow the decision-maker to change her reference distribution depending on the total amount of output (benefit). We illustrate our approach using a project portfolio selection problem. We formulate mixed integer mathematical programming models for the problem with maximizing total benefit and minimizing imbalance objectives. The bi-objective models are solved with both the epsilon-constraint method and an interactive algorithm.Item Open Access The effect of stake size in experimental bargaining and distribution games: a survey(Springer, 2017) Karagözoğlu, E.; Urhan, Ü. B.We review the literature on bargaining and distribution experiments to investigate whether changes in stake size have significant effects on behaviour in laboratory/field settings. We conclude that experiments in this field do not lead to clear/common results. The joint presence of opposing factors (e.g., increasing relative risk aversion and increasing cost of fairness) might be one reason contributing to this. Moreover, we argue that variables such as subjects’ financial conditions, cognitive abilities, risk attitudes, loss-aversion, justice orientations, and relevant personality characteristics should be controlled in laboratory experiments to understand the effect of stake size on behaviour, more clearly. Finally, quasi-experiments using data from (very) high-stake games/events and meta-analysis studies should complement (individual) controlled experiments. � 2016, Springer Science+Business Media Dordrecht.Item Open Access The effects of reference points on fairness judgments(Bilkent University, 2014) Akar, BetulIn this study, we empirically investigate the effects of reference points on fairness judgments with the help of the vignette technique. Specifically, we examine (i) whether reference points have influence on fairness judgments or not, (ii) if and how counter-intuitive reference points influence fairness judgments, and (iii) how the asymmetry of reference points shape fairness judgments. For that purpose, we use a within-subject design, in which participants are confronted with three versions of vignette: vignettes without reference point, vignettes with salient reference point, and vignettes with counter-intuitive reference point. Consequently, our findings suggest that (i) the reference points significantly influence fairness judgments, (ii) introducing counter-intuitive reference points moderate the salience of reference points, and (iii) the asymmetry of salient reference points has a concave relationship with fairness judgments, while the asymmetry of counter-intuitive reference points does not affect fairness judgments.Item Open Access Ensuring multidimensional equality in public service(Elsevier Ltd, 2021-10-23) Akoluk, Damla; Karsu, ÖzlemService planning problems typically involve decisions that lead to the distribution of multiple benefits to multiple users, and hence include equality and efficiency concerns in a multidimensional way. We develop two mathematical modeling-based approaches that incorporate these concerns in such problems. The first formulation aggregates the multidimensional efficiency and equality (equitability) concerns in a biobjective model. The second formulation defines an objective function for each benefit, which maximizes the total social welfare obtained from that specific benefit distribution; this results in an n-objective model, where n is the number of benefits. We illustrate and compare these approaches on an example public service provision problem.Item Open Access Ensuring multidimensional fairness in public service(Bilkent University, 2020-12) Akoluk, DamlaIn this study, we focus on service planning problems, in which decisions lead to distributions of multiple benefits to multiple users, hence involve fairness and efficiency concerns in a multidimensional way. We develop two mathematical modeling-based approaches that incorporate these concerns in such problems. The first formulation aggregates the multidimensional efficiency concerns and multidimensional fairness concerns in a bi-objective model. The second formulation defines an objective function for each benefit, which maximizes the total social welfare obtained from that specific benefit distribution, hence results in an nobjective model, where n is the number of benefits. We illustrate and compare these approaches on an example public service provision problem.Item Open Access Equitable decision making approaches over allocations of multiple benefits to multiple entities(Bilkent University, 2017-07) Keleş, Sema Nur KaynarIn this study, we develop decision support tools for policy makers that will help them make choices among a set of allocation alternatives. We assume that alternatives are evaluated based on their benefits to different users and that there are multiple benefit (output) types to consider. We assume that the policy maker has both efficiency (maximizing total output) and equity (distributing outputs across different users as fair as possible) concerns. This problem is a multicriteria decision making problem where the alternatives are represented with matrices rather than vectors. We develop interactive algorithms that guide a policy maker to her most preferred solution (a set of most preferred solutions), which are based on utility additive (UTA) and convex cone methods. Our computational experiments demonstrate the satisfactory performance of the algorithms. We believe that such decision support tools may be of great use in practice and help in moving towards fair and efficient allocation decisions.Item Open Access Equitable decision making approaches over allocations of multiple benefits to multiple entities(Elsevier, 2018) Kaynar, N.; Karsu, ÖzlemIn this study, we develop decision support tools for policy makers that will help them make choices among a set of allocation alternatives. We assume that alternatives are evaluated based on their benefits to different users and that there are multiple benefit (output) types to consider. We assume that the policy maker has both efficiency (maximizing total output) and equity (distributing outputs across different users as fair as possible) concerns. This problem is a multicriteria decision making problem where the alternatives are represented with matrices rather than vectors. We develop interactive algorithms that guide a policy maker to her most preferred solution, which are based on utility additive (UTA) and convex cone methods. Our computational experiments demonstrate the satisfactory performance of the algorithms. We believe that such decision support tools may be of great use in practice and help in moving towards fair and efficient allocation decisions.Item Open Access Fair allocation of personal protective equipment to health centers during early phases of a pandemic(Elsevier, 2022-05) Dönmez, Zehranaz; Turhan, S.; Karsu, Özlem; Kara, Bahar Y.; Karaşan, OyaWe consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number of infected health workers, the number of infected patients and minimizing a deprivation cost function defined over shortages. We solve the resulting problem using the ε-constraint algorithm so as to obtain the exact Pareto set. We also develop a customized genetic algorithm to obtain an approximate Pareto frontier in reasonable time for larger instances. We provide a comparative analysis of the exact and heuristic methods under various scenarios and give insights on how the suggested allocations outperform the ones obtained through a set of rule-of-thumb policies, policies that are implemented owing to their simplicity and ease-of-implementation. Our comparative analysis shows that as the circumstances get worse, the trade-off between the deprivation cost and the ratio of infections deepens and that the proposed heuristic algorithm gives very close solutions to the exact Pareto frontier, especially under pessimistic scenarios. We also observed that while some rule-of-thumb policies such as a last-in-first-receives type policy work well in terms of deprivation costs in optimistic scenarios, others like split policies perform well in terms of number of infections under neutral or pessimistic settings. While favoring one of the objectives, these policies typically fail to provide good solutions in terms of the other objective; hence if such policies are to be implemented the choice would depend on the problem characteristics and the priorities of the policy makers. Overall, the solutions obtained by the proposed methods imply that more complicated distribution schemes that are not induced by these policies would be needed for best results.Item Open Access Fair resource allocation: Using welfare-based dominance constraints(Elsevier, 2022-03-01) Argyris, N.; Karsu, Özlem; Yavuz, MirelIn this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem.Item Open Access Fair task allocation in crowdsourced delivery(Institute of Electrical and Electronics Engineers, 2018) Basik, F.; Gedik, B.; Ferhatosmanoglu, H.; Wu, K.Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries. In this work, we introduce a new crowdsourced delivery platform that takes fairness towards workers into consideration, while maximizing the task completion ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase assignment model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that maximizes fairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task completion ratio, as well as fairness and assignment ratio. Experiments show that F-Aware runs around $10^7\times$ faster than the TAR-optimal solution and assigns 96.9% of the tasks that can be assigned by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm. IEEEItem Open Access Finding robustly fair solutions in resource allocation(Bilkent University, 2022-07) Elver, İzzet EgemenIn this study, we consider resource allocation problems where the decisions affect multiple beneficiaries and the decision maker aims to ensure that the effect is distributed to the beneficiaries in an equitable manner. We specifically consider stochastic environments where there is uncertainty in the system and propose a robust programming approach that aims at maximizing system efficiency (measured by the total expected benefit) while guaranteeing an equitable benefit allocation even under the worst scenario. Acknowledging the fact that the robust solution may lead to high efficiency loss and may be over-conservative, we adopt a parametric approach that allows controlling the level of conservatism and present the decision maker alternative solutions that reveal the trade-off between the total expected benefit and the degree of conservatism when incorporating fairness. We obtain tractable formulations, leveraging the results we provide on the properties of highly unfair allocations. We demonstrate the usability of our approach on project selection and shelter allocation applications.Item Open Access In Press, Corrected Proof: Ensuring multidimensional equality in public service(Elsevier, 2021-10-23) Akoluk, Damla; Karsu, ÖzlemService planning problems typically involve decisions that lead to the distribution of multiple benefits to multiple users, and hence include equality and efficiency concerns in a multidimensional way. We develop two mathematical modeling-based approaches that incorporate these concerns in such problems. The first formulation aggregates the multidimensional efficiency and equality (equitability) concerns in a biobjective model. The second formulation defines an objective function for each benefit, which maximizes the total social welfare obtained from that specific benefit distribution; this results in an n-objective model, where n is the number of benefits. We illustrate and compare these approaches on an example public service provision problem.Item Open Access Inequity averse optimization in operational research(Elsevier, 2015) Karsu, Ö.; Morton, A.There are many applications across a broad range of business problem domains in which equity is a concern and many well-known operational research (OR) problems such as knapsack, scheduling or assignment problems have been considered from an equity perspective. This shows that equity is both a technically interesting concept and a substantial practical concern. In this paper we review the operational research literature on inequity averse optimization. We focus on the cases where there is a tradeoff between efficiency and equity. We discuss two equity related concerns, namely equitability and balance. Equitability concerns are distinguished from balance concerns depending on whether an underlying anonymity assumption holds. From a modeling point of view, we classify three main approaches to handle equitability concerns: the first approach is based on a Rawlsian principle. The second approach uses an explicit inequality index in the mathematical model. The third approach uses equitable aggregation functions that can represent the DM's preferences, which take into account both efficiency and equity concerns. We also discuss the two main approaches to handle balance: the first approach is based on imbalance indicators, which measure deviation from a reference balanced solution. The second approach is based on scaling the distributions such that balance concerns turn into equitability concerns in the resulting distributions and then one of the approaches to handle equitability concerns can be applied. We briefly describe these approaches and provide a discussion of their advantages and disadvantages. We discuss future research directions focussing on decision support and robustness.Item Open Access The influence of distributive justice on lying for and stealing from a supervisor(Springer Netherlands, 2009) Umphress, E. E.; Ren, L. R.; Bingham, J. B.; Gogus, C. I.In a controlled laboratory experiment, we found evidence for our predictions that participants who received fair distributive treatment were more likely to lie to give a supervisor a good performance evaluation than those treated unfairly, and those who received unfair distributive treatment were more likely to steal money from a supervisor than those treated fairly. We further proposed that the presence of an ethical code of conduct would moderate these relationships such that when the code was present these relationships would be weaker than when the code was absent, but we failed to find support for these moderating effects. Our findings suggest that the relationship between distributive justice and unethical behavior is likely more complex than previously considered. Both researchers and managers may benefit from a broader understanding of the factors that motivate and inhibit unethical behaviors intended to benefit and harm supervisors and/or organizations.Item Open Access Morality in competition law: the culture of honesty and trust in consumer protection(Athens Institute for Education and Research, 2021-04) Uçaryılmaz, TalyaRecent works in legal scholarship have shifted the focus of competition law to the economic analysis of law. Yet today we face the revival of the fairness concerns in competition polices. This article concerns itself with the nature of the interdependent relationship between competition law and consumer protection law as ancillary to the necessary relationship between law and morality. Hereby it aims to revisit their raison d’être to discuss that fairness and equity do not lack economic foundations. For an efficient market structure, private property and good faith in contractual relations are essential. This article aims to scrutinise the latter, while showing its objective criteria: Honesty, trust and reasonableness, as the moral essence of competition and consumer protection laws. These criteria provide efficient means to address moral aspects of fairness in competition law as it is best illustrated within its relation to consumer protection without compromising their economic foundations.Item Open Access A new ex-ante efficiency criterion and implications for the probabilistic serial mechanism(Academic Press, 2018) Doğan, B.; Doğan, Serhat; Yıldız, KemalWe introduce and analyze an efficiency criterion for probabilistic assignment of objects, when only ordinal preference information is available. This efficiency criterion is based on the following domination relation: a probabilistic assignment dominates another assignment if it is ex-ante efficient for a strictly larger set of utility profiles consistent with the ordinal preferences. We provide a simple characterization of this domination relation. We revisit an extensively studied assignment mechanism, the Probabilistic Serial mechanism (Bogomolnaia and Moulin, 2001), which always chooses a “fair” assignment. We show that the Probabilistic Serial assignment may be dominated by another fair assignment. We provide conditions under which the serial assignment is undominated among fair assignments.Item Open Access Random access over wireless links: optimal rate and activity probability selection(Bilkent University, 2017-07) Karakoç, NurullahDue to the rapidly increasing number of devices in wireless networks with the proliferation of applications based on new technologies such as machine to machine communications and Internet of Things, there is a growing interest in the random access schemes as they provide a simple means of channel access. To this end, various schemes have been proposed based on the ALOHA protocol to increase the e ciency of the medium access control layer over the last decade. On the other hand, physical layer aspects of random access networks have received relatively limited attention, and there is a need to consider optimal use of the underlying physical layer properties especially for transmission over wireless channels. In this thesis, we study uncoordinated random access schemes over wireless fading channels where each user independently decides whether to send a packet or not to a common receiver at any given time slot. To characterize the system throughput, i.e., the expected sum-rate, an information theoretic formulation is developed. We consider two scenarios: classical slotted ALOHA, where no multiuser detection (MUD) capability is available and slotted ALOHA with MUD. Our main contribution is that the optimal rates and the channel activity probabilities can be characterized as a function of the user distances to the receiver to maximize the system throughput in each case (more precisely, as a function of the average signal to noise ratios of the users). We use Rayleigh fading as our main channel model, however, we also study the cases where log-normal shadowing is observed along with small scale fading. Our proposed optimal rate selection schemes o er signi cant increase in expected system throughput compared to the same rate approach commonly used in the literature. In addition to the overall throughput optimization, the issue of fairness among users is also investigated and solutions which guarantee a minimum amount of individual throughput are developed. We also design systems with limited individual outage probabilities of the users for increased energy e ciency and reduced delay. All of these analytical works are supported with detailed numerical examples, and the performance of the proposed methods are evaluated.Item Open Access Solution approaches for equitable multiobjective integer programming problems(Springer, 2022-04) Bashir, Bashir; Karsu, ÖzlemWe consider multi-objective optimization problems where the decision maker (DM) has equity concerns. We assume that the preference model of the DM satisfies properties related to inequity-aversion, hence we focus on finding nondominated solutions in line with the properties of inequity-averse preferences, namely the equitably nondominated solutions. We discuss two algorithms for finding good subsets of equitably nondominated solutions. The first approach is an extension of an interactive approach developed for finding the most preferred nondominated solution when the utility function is assumed to be quasiconcave. We find the most preferred equitably nondominated solution when the utility function is assumed to be symmetric quasiconcave. In the second approach we generate an evenly distributed subset of the set of equitably nondominated solutions to be considered further by the DM. We show the computational feasibility of the two algorithms on equitable multi-objective knapsack problem, in which projects in different categories are to be funded subject to a limited budget. We perform experiments to show and discuss the performances of the algorithms. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Open Access Two approaches for fair resource allocation(Bilkent University, 2018-05) Yavuz, MirelFairness has become one of the primary concerns in several Operational Research (OR) problems, especially in resource allocation problems. It is crucial to ensure a fair distribution of the resources across the entities so that the proposed solutions will be both applicable and acceptable. In many real-life systems, the most e cient solution will not be the most fair solution, which creates a trade-o between e ciency and fairness. We propose two approaches in order to help the decision makers (DM) to nd an e cient solution which take fairness of the distribution of resources into account. First approach we propose is optimizing a speci c subset of the set of Schur-concave functions, namely ordered weighted averaging (OWA) functions, which are able to re ect both e ciency and fairness concerns. We do not assume that the weights of the DM to be used in OWA functions are readily available. We explore a wide range of weight vectors and report results for these di erent choices of weights. We illustrate the approach using a workload allocation problem and a knapsack problem and visualize the trade-o between fairness and e ciency. In some applications, the DM may provide a reference point such that the aim would be nding an e cient solution which is more preferable than this reference in terms of fairness. For such cases we propose a second approach that maximizes e ciency while controlling fairness concerns via a constraint. Similar to the rst approach, fairness concerns are re ected using OWA function forms. However, the resulting formulation yields to non-linearity. Thus, a hybrid interactive algorithm is presented that tackles this nonlinearity using an enumerative approach. The algorithm nds an e cient solution which OWA dominates the reference point by interacting with the DM. The algorithm is tested on knapsack problems and shows successful performance.