Fair Equilibrium Research Articles

An integrated diagnostic framework for water resource spatial equilibrium considering water-economy-ecology nexus

The mismatch of spatial distribution between water resources and human activities presents a considerable challenge to the sustainable utilization of water resources. This trend is further exacerbated by the impact of climate change and the increasingly frequent occurrence of extreme weather. Therefore, it is imperative to conduct an in-depth study on water resource spatial equilibrium (WRSE). This paper proposed an integrated diagnostic framework for WRSE considering the water-economy-ecology nexus, in which an improved evaluation index system of WRSE was constructed. The WRSE states of three criteria layers are quantified with the spatial equilibrium indices of water resource endowment, economic and social development, and ecological environment protection. The water resource spatial equilibrium index (WRSEI) was calculated using the Euclidean distance to reflect the overall spatial equilibrium state. The key influencing factors of WRSE are analyzed through the obstacle degree model. The findings indicate that: a) the WRSEI in the Yangtze River to Huaihe River Water Transfer Project (YHWTP) area showed a gradual increase from 0.641 in 2011 to 0.662 in 2020; b) the WRSE level of each criterion layer attained a general equilibrium or fair equilibrium state, and the WRSE state shows a trend from dispersion to aggregation during 2011 and 2020; c) per capita water resources, per capita gross domestic product and per capita urban wastewater discharge are the main factors limiting the realization of a benign WRSE in the YHWTP, with the obstacle degrees reaching 0.347, 0.324 and 0.326 in 2020 respectively. This framework can provide a theoretical reference for the study of WRSE, and provide a scientific basis and suggestions for policymakers to enhance WRSE and achieve sustainable utilization of water resources.

Anti-Windup Coordination Strategy Around a Fair Equilibrium in Resource Sharing Networks

We coordinate interconnected agents where the control input of each agent is limited by the control input of others. In that sense, the systems have to share a limited resource over a network. Such problems can arise in different areas and it is here motivated by a district heating example. When the shared resource is insufficient for the combined need of all systems, the resource will have to be shared in an optimal fashion. In this scenario, we want the systems to automatically converge to an optimal equilibrium. The contribution of this paper is the proposal of a control architecture where each separate system is controlled by a local PI controller. The controllers are then coordinated through a global rank-one anti-windup signal. It is shown that the equilibrium of the proposed closed-loop system minimizes the infinity-norm of stationary state deviations. A proof of linear-domain passivity is given, and a numerical example highlights the benefits of the proposed method with respect to the state-of-the-art.

A rational quantum state sharing protocol with semi-off-line dealer

A rational quantum state sharing protocol with the semi-off-line dealer is proposed. Firstly, the dealer Alice shares an arbitrary two-particle entangled state with the players by Einstein–Podolsky–Rosen (EPR) pairs and Greenberger–Horne–Zeilinger (GHZ) states. The EPR pairs are prepared by Charlie instead of the dealer, reducing the workload of the dealer. Secondly, all players have the same probability of reconstructing the quantum state, guaranteeing the fairness of the protocol. In addition, the dealer is semi-off-line, which considerably reduces the information exchanging between the dealer and the players. Finally, our protocol achieves security, fairness, correctness, and strict Nash equilibrium.

Multidimensional Fair Fuzzy Equilibrium Evaluation of Housing Expropriation Compensation from the Perspective of Behavioral Preference: A Case Study from China

With the rapid development of urbanization, substantial land areas and houses are expropriated, which can cause huge numbers of disputes related to expropriation compensation. The root of the disputes is that the associated subjects are affected by various behavioral preferences and make different cognitive fairness judgments based on the same compensation price. However, the existing expropriation compensation strategies based on the market value under the assumption of “the economic man” hypothesis cannot meet the fairness preference demands of the expropriated. Therefore, finding a compensation price that satisfies subjects’ multidimensional fairness preferences, including profit-seeking, loss aversion, and interactive fairness preferences, is necessary. Only in this way can the subjects reach an agreement regarding fair compensation and resolve their disputes. Because of the fuzziness of subjects’ expected revenues, this paper innovatively introduces trigonometric intuitional fuzzy numbers to construct one-dimensional and multidimensional fair fuzzy equilibrium evaluation models. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is adopted to convert a multidimensional problem into a multiattribute group decision problem, which simplifies the problem of finding multidimensional equilibrium when considering the multidimensional fairness preferences of the two subjects. Real case data are introduced to verify the validity of this method. The research results show that upward revision of the multidimensional fairness preferences based on the market value assists in achieving a fair compensation agreement. Consideration of the influence of the subjects’ multidimensional fairness preferences on the fairness equilibrium is conducive to resolving the disputes, and provides a reference for the settlement of expropriation compensation disputes in developing countries.

Urban form and livability: socioeconomic and built environment indicators

Spatial relations among urban elements (buildings, streets, etc.) constantly affect the quality of urban spaces, creating more or less livable cities. The study of urban form has been a way of objectively quantifying such relations to understand their dynamics. Urban livability is the ability of urban spaces to fulfill the expectations of its inhabitants for wellbeing and quality of life. Measurable spatial patterns underlie the emergence of livable cities. Still, few researchers have considered if and how these patterns affect socioeconomic conditions across spatial scales. This paper explores the relationships between indicators of socioeconomic livability and cross-scale patterns of demographic and morphological densities within the Metro Vancouver (MV) region (Canada). Indicators of accessibility, social diversity, affordability, and economic vitality were quantified and compared among five population density clusters composed of 3450 census dissemination areas (DAs) in MV. Morphological indicators of intensity, centrality and diversity were aggregated at the DAs using spatial network analysis with five radii from 400 to 4800 m. Socioeconomic indices were regressed on urban form variables to assess the importance of the built environment on predicting livability-related qualities. Overall, indicators of the intensity of urban form were the most significant to predict the socioeconomic metrics. <strong><em>Policy relevance</em><br /></strong> Policies that aim to solve urban issues should consider nonlinear relations among variables. In the case of MV, indicators of accessibility, social diversity and economic vitality are directly correlated with each other and inversely correlated with affordability. Medium to high-density zones presented a fair equilibrium among the different livability qualities analyzed. Attributes aggregated with the 4800 m radius were highly important to predict the livability qualities within a 400 m radius, which potentially means that urban interventions may affect the livability of spaces not immediately close to them. A higher density of buildings with moderate height distributed among parcels with distinct sizes can potentially have a positive impact on economic vitality and housing affordability. The intensity and diversity of the tree canopy was important to predict active accessibility and social diversity. The inclusion of spatial diversity and network centrality measures on urban planning and design practices potentially foster more livable densification processes.

Multidimensional Fairness Equilibrium Evaluation of Urban Housing Expropriation Compensation Based on VIKOR

Against the backdrop of emerging markets and the transitional society, the large-scale start-up of real estate development projects has brought about rapid economic growth and accelerated urban expansion, followed by extreme disputes between social groups. This paper aims to effectively solve the real dilemma of urban housing expropriation by obtaining a consensus regarding the fairness of compensation standards among expropriation compensation-related subjects. Three behavioral preferences—profit-seeking fairness, loss aversion and interactive fairness—were added to a multidimensional fairness equilibrium evaluation indicator system of urban housing expropriation compensation. The entropy method was used to calculate their weights. A multidimensional fairness game model and a multidimensional fairness equilibrium evaluation method based on compromise multi-criteria decision-making VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) of urban housing expropriation compensation were constructed to combine different strategic schemes of related subjects for the purpose of obtaining the compromise optimal solution, that is, the multidimensional fairness game equilibrium solution. The stability of the multidimensional fairness game model and the objectivity of the multidimensional fairness equilibrium evaluation were tested and verified through case data analysis and sensitivity analysis. The conclusion is drawn that the multidimensional fairness game equilibrium solution can effectively resolve extreme disputes regarding urban housing expropriation.

A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning

This paper presents FENOCOP, a game-theoretic approach for solving non-cooperative planning problems that involve a set of self-interested agents. Each agent wants to execute its own plan in a shared environment but the plans may be rendered infeasible by the appearance of potential conflicts; agents are willing to coordinate their plans in order to avoid conflicts during a joint execution. In order to attain a conflict-free combination of plans, agents must postpone the execution of some of their actions, which negatively affects their individual utilities. FENOCOP is a two-level game approach: the General Game selects a Nash equilibrium among several combinations of plans, and the Scheduling Game generates, for a combination of plans, an executable outcome by introducing delays in the agents’ plans. For the Scheduling Game, we developed two algorithms that return a Pareto optimal and fair equilibrium from which no agent would be willing to deviate.

Collective risk social dilemma and the consequences of the US withdrawal from international climate negotiations

International climate negotiations represent an interesting theoretical problem, which can be analyzed as a collective risk social dilemma as well as an n-person bargaining model. The problem is made more complicated by politics due to the differences between: (1) total and per capita emissions; and (2) present-day and cumulative emissions. Here, we use a game theoretic approach in conjunction with the literature on effort-sharing approaches to study a model of climate negotiations based on empirical emissions data. We introduce a ‘fair equilibrium’ bargaining solution and examine the consequences of the United States’ withdrawal from the Paris Agreement. Our results suggest that the collective goal can still be reached but that this requires additional greenhouse gas emissions cuts from other countries, notably, China and India. Given the history of climate negotiations, it is unclear if these countries will have sufficient political will to accept the additional costs created by the US defection.

SILP: A Stochastic Imitative Learning Protocol for Multi-Carrier Spectrum Access

Decentralized wireless networks require efficient channel access protocols to enable wireless nodes (WNs) to access dedicated frequency channels without any coordination. In this paper, we develop a distributed spectrum access protocol for the case where the WNs are equipped with multiple radio transceivers. We consider the case where the channels are identical and duly separated so that each of the users’ antenna can access only one of the available channels. To model the competition amongst WNs, we formulate a particular multi-agent multi-carrier spectrum access game, where each WN has to decide at each iteration how many antennas and which frequency channels it has to access. To study the resulting equilibrium, we solve a multi-objective optimization problem and design a bi-level learning algorithm which is proven to converge toward a socially efficient and max–min fair equilibrium state.

Reproducible measurements of TCP BBR congestion control

The complexity of evaluating TCP congestion control has increased considerably since its initial development in the 1980s. Several congestion control algorithms following different approaches have been proposed to match the requirements of modern computer networks. We present a framework to analyze different congestion control algorithms using network emulation. The framework is publicly available which provides easy repeatability of our experiments and reproducibility of the results.As a case study, we use our framework to analyze the bottleneck bandwidth and round-trip time (BBR) congestion control algorithm, which was published by Google in 2016. Because of promising initial results, BBR has gained wide-spread attention. As such it has been subject analysis, which showed an increase in performance, but also revealed critical flaws. We verify our framework by reproducing experiments from related work which confirm weaknesses of the current BBR implementation. We also contribute an analysis of BBR’s inter-flow synchronization behavior and its interaction when competing with other congestion control algorithms. Our results show that BBR flows on their own have difficulty to reach a fairness equilibrium and suppress other congestion control algorithms. BBR is still work in progress, thus the framework is useful to validate further updates of BBR by rerunning the given experiments.

A complex systems model for transformative supply chains in emerging markets

PurposeCorporations operating global value chains must grapple with a multiplicity of ethical and practical considerations, most notably when value chains extend to emerging markets. Such contexts involve interactions with diverse stakeholders who possess the ability to impact supply chain performance, but who also bring conflicting needs, values and interests. The purpose of this paper is to outline a transformative model of supply chain fairness, arguing that adopting plural fairness principles and practices generates a higher fairness equilibrium which includes all affected stakeholders in the production of fairness outcomes, with consequent positive organizational and system level impacts.Design/methodology/approachThrough a philosophically informed overview of the literature on organizational fairness, the paper applies fairness to the management of supplier relations to identify the institutional features of ethically sustainable supply chains. The proposed conceptual model uses a complex adaptive systems approach (CADs), supplemented by describing the contribution of fairness norms and practices.FindingsThis paper argues that a transformative approach to supply chain fairness can suggest new structures for interaction between firms, stakeholders, mediating institutions and governments.Originality/valueEmerging market supply chains are facing significant changes. Adopting a complex adaptive systems perspective upon stakeholder relationships, this paper offers insights from the theoretical literature on fairness, and proposes a normative model of supply chain fairness which accounts for both the normative and empirical aspects of relational complexity.

Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach

Cooperation is an emergent social state related to the dynamics and complexity of road traffic and is reinforced through adaptive learning. Game theory and research in behavioural economics provide ample evidence that cooperation can efficiently solve social dilemmas similar to traffic congestion in dynamic settings. Traffic theory, asserts User Equilibrium, is both a stable and equitable, albeit inefficient, network state, which is a behavioural outcome of the selfish uncoordinated decision of drivers. In contrast, the System Optimum is an efficient network state that minimizes the total travel costs but is hard to maintain due to the inherent cost inequalities drivers will incur. In this paper, we describe how the principles of game-theory in a simple 2-player game allow the emergence of a stable system optimum through cooperation. We then investigate what happens in n-player games by applying an agent-based route-choice model. The model shows how reinforced learning and different behavioural specifications regarding agents’ cognition – selfish or cooperative - brings a simple road network from User Equilibrium towards the system optimum while preserving sufficient equity amongst drivers. The results suggest that a sufficient number of route alternations between drivers and a certain degree of altruism allow for a self-organizing formation of a fairness equilibrium that can maintain the network in the system optimum. The implications of future congestion management strategies that can be implemented with information and communication technologies are discussed.

Examining the effect of quantum strategies on symmetric conflicting interest games

The explicit construction is presented of two-player game satisfying: (i) symmetry with respect to the permutation of the players; (ii) the existence of upper bound on total payoff following from Bell inequality; (iii) the existence of unfair equilibrium with total payoff saturating the above bound. The quantum counterpart of the game is considered which possesses only fair equilibria and strategies outperforming the classical ones.

Three-player conflicting interest games and nonlocality

We outline the general construction of three-player games with incomplete information which fulfil the following conditions: (i) symmetry with respect to the permutations of players; (ii) the existence of an upper bound for total payoff resulting from Bell inequalities; (iii) the existence of both fair and unfair Nash equilibria saturating this bound. Conditions (i)–(iii) imply that we are dealing with conflicting interest games. An explicit example of such a game is given. A quantum counterpart of this game is considered. It is obtained by keeping the same utilities but replacing classical advisor by a quantum one. It is shown that the quantum game possesses only fair equilibria with strictly higher payoffs than in the classical case. This implies that quantum nonlocality can be used to resolve the conflict between the players.

A Spatial Game Theoretic Analysis of Conflict and Identity

Using theoretical arguments and extensive simulations, in this paper we conclude that in a spatial evolutionary Hawk–Dove game with uncorrelated asymmetry and informational asymmetry, the evolutionarily stable strategy against multiple mutations (ESSMM) largely coincides with the fairness equilibria of Hawk–Dove game. The shift from one fairness equilibrium to another bases itself on risk dominance criterion. Moreover, we notice that in spatial games the ESSMM turns out to be pure strategy except in the case where every player has prior information about the identity of the other player. Our results has interesting implications for designing fair behaviour inducing mechanisms.

Game-Theory-Based Distributed Power Splitting for Future Wireless Powered MTC Networks

This paper studies the emerging wireless power transfer for machine type communication (MTC) network, where one hybrid access point (AP) with constant power supply communicates with a set of users (i.e., wearable devices and sensors) without power supply. The information and energy are transferred simultaneously in downlink direction. For MTC networks, most devices only receive several bits control data from AP in downlink transmission. So it is possible to utilize part of the received power to execute energy harvesting provided that the transmission reliability is guaranteed. Since we assume that all devices are without power supply or battery, the power of uplink transmission is entirely from energy harvesting. After converting electromagnetic wave to electricity, the devices are able to transmit their measured and collected data in uplink. Based on these considerations, a non-cooperative game model is formulated and a utility function involving both downlink decoding signal to noise ratio (SNR) and uplink throughput is established. The existence of Nash equilibrium (NE) in the formulated game model is proved. The uniqueness of NE is discussed and the expected NE is selected based on fairness equilibrium selection mechanism. The optimal splitting ratio within the feasible set, which maximizes the utility function, is obtained by an iterative function derived from this utility function. The numerical results show that in addition to ensuring the downlink decoding SNR and maximizing uplink throughput of an individual device, our proposed algorithm outperforms the conventional algorithm in terms of system performance.

Nonlocal correlations: Fair and unfair strategies in Bayesian games

Interesting connection has been established between two apparently unrelated concepts, namely, quantum nonlocality and Bayesian game theory. It has been shown that nonlocal correlations in the form of advice can outperform classical equilibrium strategies in common interest Bayesian games and also in conflicting interest games. However, classical equilibrium strategies can be of two types, fair and unfair. Whereas in fair equilibrium payoffs of different players are same, in unfair case they differ. Advantage of nonlocal correlation has been demonstrated over fair strategies. In this work we show that quantum strategies can outperform even the unfair classical equilibrium strategies. For this purpose we consider a class of two players games which as a special case includes the conflicting game proposed in [Phys. Rev. Lett. 114, 020401 (2015)]. These games can have both fair and unfair classical equilibria and also can have only unfair ones. We provide a simple analytic characterization of the nonlocal correlations that are advantageous over the classical equilibrium strategies in these games.

An Experiment on Forward vs. Backward Induction: How Fairness and Level k Reasoning Matter

Abstract We report the experimental results on a game with an outside option where forward induction contradicts with backward induction based on a focal, risk dominant equilibrium. The latter procedure yields the equilibrium selected by Harsanyi and Selten’s (1988) theory, which is hence here in contradiction with strategic stability (Kohlberg and Mertens, 1986). We find the Harsanyi-Selten solution to be in much better agreement with our data. Since fairness and bounded rationality seem to matter we discuss whether recent behavioral theories, in particular fairness theories and learning, might explain our findings. The fairness theories by Bolton and Ockenfels (2000), Charness and Rabin (2002), or Fehr and Schmidt (1999), when calibrated using experimental data on dictator- and ultimatum games, indeed predict that forward induction should play no role for our experiment and that the outside option should be chosen by all sufficiently selfish players. However, there is a multiplicity of ‘fairness equilibria’, some of which seem to be rejected because they require too many levels of reasoning. We show that learning theories based on naive priors could alternatively explain our results, but not that of closely related experiments.

Game Theory Framework for MAC Parameter Optimization in Energy-Delay Constrained Sensor Networks

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.

Equality of Arms, Albanian Case and the European Court of Human Rights

One of the main principles of a fair judicial process is the equality of arms, an elaborated principle from the Albanian Constitutional Court Jurisprudence in full harmony with the definitions made in the European Court of Human Rights. This principle implies that anyone that is a party in the judicial process must have equal possibility to introduce its issues, and that no party must satisfy any considerable advantage against the opponent, establishing therefore a fair equilibrium between these parties. This principle finds regulation from a series of judicial norms, which are sanctioned in the Albanian Constitution as well as in the way the Penal Procedure Code sounds like. In this code, we find the sanctions of the principle for an accusing process, based on the contradiction of the penal law, providing procedural means which are equal for both parties in the trial. DOI: 10.5901/ajis.2015.v4n3p181