Payoff Table Research Articles

Interactive on-ramp merging decision-making with motion prediction considering merging motivation

On-ramp is one of the scenarios most likely to cause traffic accidents and congestion. In order to mitigate the conflict between mainline vehicles and ramp vehicles, and improve vehicle safety and travel efficiency, we propose an interactive on-ramp merging decision-making (IORMDM) method based on motion prediction. We first propose a motion prediction method for different actions of vehicles based on model predictive control. Meanwhile, the game theory is employed to construct the interaction behavior between the ramp vehicle and the mainline vehicle, and the merging motivation of the ramp vehicle is modeled to describe the collision risk between it with the end of the acceleration lane. Afterward, according to the predicted trajectories of vehicles, the comprehensive payoff of vehicle collision risk, efficiency, passenger comfort, and merging motivation of each action is solved. The Nash equilibrium solution is solved to determine the optimal action of each vehicle according to the payoff table. Simulation experiments demonstrate that our proposed method could reduce collision risk and improve traffic efficiency.

Competitive behaviour of major GSM firms’ internet data pricing in Nigeria: A game theoretic model approach

The study investigated the competitive behaviour of major GSM service providers’ Internet data pricing in Nigeria. Two null hypotheses were tested: The major GSM service providers in Nigeria do not have a dominant pricing strategy; and there is no Nash Equilibrium for the pricing strategies of the major GSM service providers in Nigeria. The design was a longitudinal study of the active GSM subscribers and Internet data prices of major GSM service providers in Nigeria (MTN and the others). Two pricing strategies were employed, “charge N1000 per data plan” and “charge N1200 per data plan”. A zero-sum payoff table was obtained from the data on GSM subscribers and Internet bundle prices in Nigeria. The payoff table was analysed using mixed strategy approach. The results revealed that each player has a dominant strategy. The game has a Pareto optimal Nash equilibrium with disparate dominant strategies for both players. The Nash equilibrium is that the other GSM firms charge N1000 per internet data plan while MTN charges N1200 per Internet data plan. At a price of N1000 and N1200 per Internet data plan, the earnings of the other GSM service providers and MTN are maximised respectively irrespective of what the other player does. The study concludes that major GSM firms can use game theory to model the pricing strategy of competitors and predict the behaviour of Internet data subscribers.

GAME THEORY APPLICATION ON ONLINE TRANSPORTATION COMPANY AND DRIVER INCOME LEVELS DURING THE PANDEMIC

Online motorcycle taxi drivers are a group of people who are economically affected by the Covid-19 pandemic. This study aimed to provide a balanced choice strategy for drivers and companies. Game theory was applied to conflict of interest situations as a research method. Choices for online transportation companies and drivers are analyzed and arranged in a payoff table until they reach the saddle point. Simulation software as an illustration of a balanced model. This research resulted in driver diligence and incentive strategies as optimal strategies for drivers and companies. If drivers improve performance by choosing a driver diligence strategy, the driver's expectations of getting incentives will be more realistic. Meanwhile, for the company, when the driver's diligence increases, the choice of providing incentives will provide balanced benefits as well.

Anarchy is What Students Make of It: Playing Out Wendt’s Three Cultures of Anarchy

This article explores the hidden educational potential in the board game Diplomacy. While commonly recognized as a good low-cost negotiation simulation and a useful teaching platform, the original game version over-emphasizes the conflictual nature of international relations and presents an image of international relations that is not reflective of the state of world politics today. The article reports experiences from developing and teaching a modified version of the game. It suggests a concrete way to integrate International Relations (IR) theories directly into the game. By making three common theoretical approaches an integrated part of the game, as well as expanding game strategies, win conditions and associated payoff tables, Diplomacy can be turned from a tool for teaching one variant of Realism into a flexible platform for teaching a broader range of IR theories, including Liberal-Institutionalism and Constructivism. In this way, the gap between abstract theory and simulated practice can be bridged in a way that students much appreciate as a learning mode. The article thus suggests a partial solution to one of the key challenges of using games and simulations in political science teaching: how to connect the game or simulation with theory.

Noisy three-player dilemma game: robustness of the quantum advantage.

Games involving quantum strategies often yield higher payoff. Here, we study a practical realization of the three-player dilemma game using the superconductivity-based quantum processors provided by IBM Q Experience. We analyze the persistence of the quantum advantage under corruption of the input states and how this depends on parameters of the payoff table. Specifically, experimental fidelity and error are observed not to be properly anti-correlated; i.e., there are instances where a class of experiments with higher fidelity yields a greater error in the payoff. Further, we find that the classical strategy will always outperform the quantum strategy if corruption is higher than 50%.

Fuzzy logic aggregation of crisp data partitions as learning analytics in triage decisions

This paper provides an analytical learning system based on Fuzzy Logic AGgregation (FLAG) of crisp data partitions to improve the Triage process in a hospitality emergency department. The method compares patient rankings made by nurses with those made by an Expert to detect points for improvement. Specifically, a normalized concordance index per nurse and the average of them allow for the evaluation of the ability of the nurses of well aggregating the cases in Triage process. The proposed FLAG system is tested through an empirical case study by simulating the patients arriving at two Emergency Departments Triage. The main contribution is the definition of the global performance index combining both the nurse’s partitioning concordance with respect to the Expert’s one and the accuracy in class assignment. The empirical distribution function of the global concordance index is derived through permutation method. In this way, Kolmogorov-Smirnov testing provides the comparison of the performances of the two healthcare units. The pay-off table concordance-accuracy allows to address improvement actions. Another tool of the system is the correspondence analysis to visualize the accuracy of decisions on the class priority as well as the sharing behaviours that influence the nurse’s judgements. All this becomes part of the FLAG learning analytics system which is able to outline critical points by red flags. to improve further assignments and the overall management organization. FLAG system can be adapted in all other situations of risk where cognitive heuristics face an accuracy-effort trade-off such that their simplified decision process leads to reduced accuracy.

Bounds and dynamics for empirical game theoretic analysis

This paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex multi-agent interactions. In doing so we provide insights in the empirical meta game showing that a Nash equilibrium of the estimated meta-game is an approximate Nash equilibrium of the true underlying meta-game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the evolutionary dynamics analysis of meta-games using heuristic payoff tables (HPTs) to asymmetric games. The state-of-the-art has only considered evolutionary dynamics of symmetric HPTs in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), the dynamics of several teams of players in the capture the flag game (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the policy-space response oracle multi-agent learning algorithm.

A classification of Newcomb problems and decision theories

Newcomb-like problems are classified by the payoff table of their act-state pairs, and the causal structure that gives rise to the act-state correlation. Decision theories are classified by the one or more points of intervention whose causal role is taken to be relevant to rationality in various problems. Some decision theories suggest an inherent conflict between different notions of rationality that are all relevant. Some issues with causal modeling raise problems for decision theories in the contexts where Newcomb problems arise.

A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response

Power-to-gas (P2G) provides a new means for accommodating abandoned new energy that will support the optimal operation of virtual power plants (VPPs) in the future. In this study, a novel structure of a P2G-based virtual power plant (GVPP) is designed. A flexible risk aversion model for GVPP operation is proposed with two objectives—maximum operation profit and minimum operation risk. In the model, the conditional value at risk method and robust optimization theory are utilized to reflect uncertainty risks. To solve the multi-objective model, a solution algorithm was constructed for iteratively obtaining the optimal weight coefficient for transforming the multi-objective model into a single-objective model based on a payoff table and a rough set method. Four simulation cases were set for comparative analysis based on a nine-node energy hub system. The results show the following outcomes: (1) GVPP realizes the complementary utilization of distributed energy and forms a power-gas-power recycling mode, (2) the proposed model can provide an effective decision-making tool for different risk-attitude decision makers by setting a reasonable confidence level and robust coefficient, (3) P2G and price-based demand response (PBDR) improve the grid-connected space of clean energy, in particular, PBDR improves the flexibility of system operation and reduces operation risk, and (4) if the maximum emission trade allowance (META) is considered, P2G preferentially converts carbon dioxide into methane when the META is lower, while clean energy is preferentially used to satisfy load demand and the methane produced by P2G can be sold to the natural gas network when the META is higher. Overall, the proposed optimal decision model achieves the maximum utilization of clean energy to obtain higher economic benefits while rationally controlling operation risks; hence, providing reliable support for decision makers.

A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances

In order to make full use of distribute energy resources and decrease the abandoned energy of clean energy, the paper aggregates wind power plant (WPP), photovoltaic power generation (PV), biomass power generation (BPG), energy storage system (ESS), conventional gas turbines (CGT) and flexible load into a virtual power plant (VPP). Firstly, the basic structure and uncertainty factors of VPP operation are analyzed, and the output model of power sources is proposed. Then, a multi-objective optimization model is proposed with three objective functions, namely, the maximum operation revenue, the minimum operation risk and the minimum carbon emissions. Furthermore, the robust optimization theory is applied to construct a risk aversion model by converting the constraint conditions into stochastic constraint conditions with uncertainty factors. Thirdly, a three-stage solution algorithm is put forward for the proposed multi-objective model including payoff table establishment, fuzzy linearization and objective weight calculation. Finally, the modified IEEE30 node system is chosen as simulation system. The results show: (1) VPP could utilize the complementary nature of different distributed energy sources to balance the revenue, risk and carbon emission. (2) Robust optimization theory and conditional value at risk could describe the uncertainty risk, and, the prediction accuracy needs to be improved for controlling the operation risk. (3) The risk attitude of decision makers would affect VPP scheduling scheme, the less uncertainty lead to greater risk when confidence degree ≤0.85 and robust coefficient ≥0.95 (risk extreme aversion). (3) Price-based demand response (PBDR) could smooth load demand curve and the maximum total emission allowances (MTEA) can highlight the environmentally friendly characteristics, which could improve more grid-connected space of clean energy and achieve the optimal VPP operation. Therefore, the proposed multi-objective model could obtain higher economic benefit and achieve lower carbon emissions while rationally controlling risks, which could be taken as an reliable decision support for decision makers.

A Multi-Objective Scheduling Optimization Model for a Multi-Energy Complementary System Considering Different Operation Strategies

In order to reduce the amount of abandoned clean energy, the complementary characterization of wind power plants (WPPs), photovoltaic power plants (PVs), hydropower stations (HSs), and thermal power plants (TPPs) combined with energy storage devices (ESDs) is considered, and they are integrated into a multi-energy complementary system (MECS). Firstly, a scenario-generating technique is proposed for uncertainty factors using the Wasserstein method and the improved K-medoids theory. Then, a multi-objective model and solution algorithm are constructed under the objectives of attaining the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations. Meanwhile, the influence of different ESD operation modes on MECS operation is discussed, specifically, the longest life cycle (LLC) and the optimum economic efficiency (OEE). Thirdly, in order to solve the multi-objective model, a solution algorithm is proposed by using the rough set method to convert the multi-objective model into a single objective model based on the payoff table. Moreover, the complementary features of the MECS are evaluated in terms of the load tracking degree, HS secondary peaking capacity, and units of coal consumption. Finally, the improved IEEE 14-bus system is chosen for the simulation analysis. The results show that (1) the proposed uncertainty simulation method can efficiently generate the most representative scenarios; (2) MECSs can utilize complementary power sources, the OEE mode can better optimize MECS scheduling, and the LLC mode can ensure the ESDs’ life cycles; (3) the scheduling scheme of MECS operation reach the optimal level when the capacity ratio of ESD:WPP–PV iso [0.62, 0.77] in the OEE mode and [1, 1.08] in the LLC mode on a typical summer day, and the ratio is [0.92, 1] in the OEE mode and [1.23, 1.31] in the LLC mode on a typical winter day. Therefore, the proposed model provides effective decision-making support for designing the optimal plan for MECS operation.

A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

Symmetric Decomposition of Asymmetric Games

We introduce new theoretical insights into two-population asymmetric games allowing for an elegant symmetric decomposition into two single population symmetric games. Specifically, we show how an asymmetric bimatrix game (A,B) can be decomposed into its symmetric counterparts by envisioning and investigating the payoff tables (A and B) that constitute the asymmetric game, as two independent, single population, symmetric games. We reveal several surprising formal relationships between an asymmetric two-population game and its symmetric single population counterparts, which facilitate a convenient analysis of the original asymmetric game due to the dimensionality reduction of the decomposition. The main finding reveals that if (x,y) is a Nash equilibrium of an asymmetric game (A,B), this implies that y is a Nash equilibrium of the symmetric counterpart game determined by payoff table A, and x is a Nash equilibrium of the symmetric counterpart game determined by payoff table B. Also the reverse holds and combinations of Nash equilibria of the counterpart games form Nash equilibria of the asymmetric game. We illustrate how these formal relationships aid in identifying and analysing the Nash structure of asymmetric games, by examining the evolutionary dynamics of the simpler counterpart games in several canonical examples.

Modelling Integration of System Dinamics and Game Theory for of Financial Technology Peer to Peer Lending Industry

In Indonesia, internet users from 2013 to 2018 have increased about 52%. This growth indicates that people behaviour change in many aspects of life including in economy. One example is (Fintech P2P Lending). P2P lending is a service that offers online borrowing, lenders can loan to small business or individuals. This is in line with the Indonesia Financial Services Sector Master Plan 2015-2019. Indonesia support the rapid growth of the financial technology ecosystem, but there is need good understanding between related parties such as P2P Lending companies, lenders and borrowers. A system dynamics act as calculators to takes value to fill payoff table in game theory. Integration of system dynamics and game theory can produce the best strategy for Fintech P2P Lending in running its business while considering profit for lenders and borrowers. The best strategy is known by use non-cooperative and cooperative game theory. The result shown the best strategy for each player with non-cooperative game is when the company chooses a high level of profit margin, borrower chooses low level of debt time, and lender chooses high level of ROI. In cooperative game, the best strategy is when the company chooses a high level of profit margin, borrower chooses low level of debt time, and lender chooses low level of ROI.

Intelligent sleeping car berth inventory control system in the case of variable capacity of railcars

AbstractManaging ticket reservations in passenger rail transport is a complex task. A hybrid algorithm is developed as a decision support tool. The aim of the proposed algorithm is to provide real‐time decisions on seat inventory allocation taking into account relevant information from the past. This paper considers the revenue management application in passenger rail transport with variable capacity of sleeping cars. Uncertainty of capacity availability is embedded in the developed algorithm through a payoff table, while an Artificial Neural Network is used as a tool for making real‐time decisions. The algorithm is tested on hypothetical data.

A Solution To Rational Decision Making Via Compositional Data Analysis: A Case Study Using Students Cellular Phone Tendencies

Decisions can be simple or complex depending on the alternatives available to the decision maker, and also, to the different state of the worlds. In classical decision theory, a pay-off table is analysed in order to make an optimal decision. Here, the decision process involves the definition of different alternatives and state of the worlds, cost or profit calculations of the pay-off table, and probability values, if any, related to state of the worlds. In decision problems, sometimes, it may be necessary to involve emotional attachments in order to create pay-off table. In this setting, rather than using the usual cost or profit values, some satisfaction values can be assigned to the criteria and the rationality of different alternatives can be investigated. In this study, the rationality approach to decision pay-off matrix is demonstrated using a real life example regarding a cellular phone purchase. Furthermore, a compositional data analysis approach is also suggested, and the contributions of the compositional data analysis to decision theory are given.

Multi-objective optimization of reverse osmosis networks by lexicographic optimization and augmented epsilon constraint method

This study proposes a multi-objective optimization (MOO) of reverse osmosis (RO) networks for seawater desalination. The membrane transport model takes into consideration of the longitudinal variation of the velocity, the pressure, and the concentration in the membrane modules. The RO network with three type energy recovery device options (pressure exchanger (PX), Hydraulic Turbocharger, and turbine) is introduced. Lexicographic optimization (for calculation of a more effective payoff table) and augmented ε-constraint method (to avoid inefficient Pareto solutions) are proposed to solve the MOO problem. A fuzzy decision maker is introduced to derive the most efficient solution among Pareto-optimal solutions. Firstly, different energy recovery option studies show that using PX is seen to be the most profitable option. Exergy analysis is used to evaluate the contribution of the equipments in energy degradation. Secondly, the proposed multi-objective framework simultaneously optimizes the total annualized cost (TAC) and energy consumption. With the increases of weighting for the main objective function: TAC, the most efficient solution moves to lower TAC direction. Finally, system recovery rate is added as the third objective function. It is reasonable to stay at the appropriate system recovery rather than to increase up to its limit and generating high energetic losses.

The effect of payoff tables on experimental oligopoly behavior

We explore the effects of the provision of an information-processing instrument—payoff tables—on behavior in experimental oligopolies. In one experimental setting, subjects have access to payoff tables whereas in the other setting they have not. It turns out that this minor variation in presentation has non-negligible effects on participants’ behavior, particularly in the initial phase of the experiment. In the presence of payoff tables, subjects tend to be more cooperative. As a consequence, collusive behavior is more likely and quickly to occur.

THE IMPACT OF ENTITLEMENTS AND EQUITY ON COOPERATIVE BARGAINING: AN EXPERIMENT

I. INTRODUCTION We consider a set of bargaining situations in which the disagreement outcome, or backstop, B, is chosen by a third party. For example, a government agency may signal that it will adopt as policy any agreement that stakeholders reach among themselves; but that otherwise, it will impose a policy of its own choosing. (1) Or an arbitrator may announce the award that will be imposed if a union and employer fail to reach agreement on a new contract. Of particular interest are those situations in which the status quo allocation of resources, Q, is Pareto inferior to the imposed backstop. Although axiomatic models of cooperative bargaining (Kalai and Smorodinsky 1975; Nash 1950) presume that negotiators in such cases will condition their agreements on B and ignore Q, literatures on entitlements and focal points (Bazerman 1985; Nozick 1974) suggest that history matters. That is, bargainers may be influenced by the status quo entitlement even when it lies outside the set of outcomes that are Pareto superior to the backstop. In addition, the experimental literature on inequality aversion (e.g., Fehr and Schmidt 1999; Hoffman and Spitzer 1985; Nydegger and Owen 1975) suggests that negotiators may be attracted to outcomes that equalize gains. In this article, we use a laboratory experiment to test the standard cooperative, entitlement, and egalitarian hypotheses in a multidimensional bargaining game. We begin in Section II by describing a two-party, two-attribute bargaining space, based on the classic Edgeworth box. Drawing from the literature on cooperative bargaining, we initially hypothesize that: (1) the parties will reach an agreement, and that this agreement will be (2) Pareto efficient; (3)Pareto superior to the backstop; and (4) at the Nash bargain. As alternatives to (3) and (4), we consider two predictions from the literatures on entitlements/focal points and inequality aversion. These are that the agreements that parties reach will (5)be conditioned on the status quo allocation rather than the backstop, and (6) equalize the parties' payoffs (when the Nash bargain does not). In Section III, we describe the design of a laboratory experiment in free-form cooperative bargaining to test these hypotheses. Our design does not use the one-dimensional mechanisms usually employed to analyze bargaining outcomes--such as dictator, divide the pie, or ultimatum games. These mechanisms offer few opportunities for subjects to choose outcomes that are inefficient or Pareto inferior to the backstop, nor a clear method of differentiating the status quo allocation from the backstop. Instead, we employ an Edgeworth box game involving two subjects with Cobb-Douglas payoff functions over two goods, X and Y. We impose a Pareto inefficient backstop allocation of these goods and allow our subjects to negotiate a reallocation. If they reach an agreement, each party receives the associated value from his or her payoff table; otherwise each receives the payoff associated with the backstop. Each party's payoff table has roughly 200 possible combinations of X and Y over which he or she can bargain. In Section IV, we present our results. We find that agreement rates are high and that those agreements are mostly in, or close to, the Pareto efficient set. The Nash bargain, however, is only supported when it equalizes the value of subjects' payoffs. When the (Pareto efficient) equal payoff outcome differs from the Nash bargain, subjects tend to compromise between the two allocations, some even agreeing to allocations that are Pareto inferior to the backstop. We also find that the tendency of subjects to choose the equal payoff outcome is strengthened when they perceive that the status quo distribution had also been equitable. Section V concludes the article with a discussion of the implications of our findings. II. HYPOTHESES We motivate our design with an environmental example drawn from collaborative bargaining over the use of public lands. …

Game-Theoretic Rough Sets

This article investigates the Game-theoretic Rough Set (GTRS) model and its capability of analyzing a major decision problem evident in existing probabilistic rough set models. A major challenge in the application of probabilistic rough set models is their inability to formulate a method of decreasing the size of the boundary region through further explorations of the data. To decrease the size of this region, objects must be moved to either the positive or negative regions. Game theory allows a solution to this decision problem by having the regions compete or cooperate with each other in order to find which is best fit to be selected for the move. There are two approaches discussed in this article. First, the region parameters that define the minimum conditional probabilities for region inclusion can either compete or cooperate in order to increase their size. The second approach is formulated by having classification approximation measures compete against each other. We formulate a learning method using the GTRS model that repeatedly analyzes payoff tables created from approximation measures and modified conditional risk strategies to calculate parameter values.