Total Payoff Research Articles

Speed-accuracy trade-offs in sample-based decisions.

Success on many tasks depends on a trade-off between speed and accuracy. In a novel variant, a speed-accuracy trade-off with sample-based decisions in which both speed and accuracy jointly depend on (self-truncated) sample size, we found strong accuracy biases. On every trial of a sequential investment game, participants chose between 2 investment funds based on binary samples of the funds' past outcomes. Participants could stop sampling and decide whenever they felt sufficiently informed. Total payoff was the product of choice accuracy and number of choices completed within the available time (speed). Participants' failure to understand the dominance of speed over accuracy-that speed decreases more than accuracy improves with increasing sample size-led to dramatic oversampling. Our research aimed to examine to what extent metacognitive functions of monitoring and control could correct for the accuracy bias. Experiments 1a through 1c demonstrated similarly strong accuracy biases and payoff losses in psychology and economics students, depressed, and control patients. In Experiments 2 through 4, the accuracy bias persisted despite several manipulations (feedback, sample limit, choice difficulty, payoff, sampling truncation as default) that underlined the speed advantage, reflecting a conspicuous metacognitive deficit. Even when participants faced no risk of losing on incorrect trials but could still win on correct trials (Experiment 3) and when sampling was contingent on the active solicitation of every new element (Experiment 4), participants continued to sample too much and failed to overcome the accuracy bias. The final discussion focuses on psychological reasons and possible remedies for the metacognitive deficit in trade-off regulation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.

Optimal entry strategy of a remanufacturer and its impact on carbon reductions with online consumer reviews

ABSTRACT This study attempts to examine the effects of online consumer reviews on a remanufacturer’s entry strategy and carbon reductions. We consider a manufacturer selling a new product over two periods in online retailing, and a remanufacturer determines whether to enter the market in the first or second period to sell a remanufactured product. We find that online consumer reviews have significant impacts. First, the remanufacturer does not always choose the first-period entry strategy. Second, the remanufacturer’s earlier entry is not always detrimental to the manufacturer, but can effectively reduce carbon emissions. Third, both firms’ pricing decisions, total payoffs and carbon emission reductions are closely related to the initial consumer base in the first period, and both firms do not always benefit from online consumer reviews. Finally, online consumer reviews are beneficial to the environment when there are less primary consumers, and vice versa.

Implementing Horizontal Cooperation in Public Transport and Parcel Deliveries: The Cooperative Share-A-Ride Problem

The static share-a-ride problem (SARP) consists of handling people and parcels in an integrated way through the same vehicle, which provides a shared trip between an origin and a destination, in response to requests received in advance. When multiple providers compete on the same market (for instance, within the same city or region), horizontal cooperation can be an efficient strategy to consolidate all requests and to optimize the total payoff. This situation gives rise to the cooperative SARP (coop-SARP). In this problem, multiple depots and heterogeneous vehicles must be considered and different cooperation levels may be agreed upon by service providers. In this paper, we propose a new mathematical programming formulation for cooperative SARP along with theoretical bounds. Moreover, through numerical experiments and ad hoc statistics, we analyze the benefits of different levels of horizontal cooperation between service providers. The results show that cooperation leads to reduced travel times and to improved vehicle occupancy rates, service levels, and profits, which make such a cooperative system even more appealing for service providers.

Coalition Graph Game-Based P2P Energy Trading With Local Voltage Management

In this paper, the feasibility of peer-to-peer (P2P) energy trading in a voltage-constrained grid-connected network is studied. In particular, a local voltage management scheme is proposed that takes network constraints into consideration to instruct the prosumers to trade energy frequently in the P2P market. A coalition graph game-based P2P energy trading framework is developed, in which prosumers can form the coalition to negotiate and decide on the energy trading parameters, such as trading quantities and prices. The Myerson value rule is used to allocate the total payoff of the proposed game fairly among the participating prosumers. Further, the stability of the proposed coalition structure is confirmed. Several simulation results are provided to verify the effectiveness of the developed P2P trading model. The simulation results show that the proposed P2P trading framework can enable prosumers to export power without causing high voltage problem in the network, and help prosumers cut down a significant portion of their overall electricity costs compared to the feed-in-tariff and coalition game model without mutual negotiations.

Cohesive efficiency in TU-games: axiomatizations of variants of the Shapley value, egalitarian values and their convex combinations

We relax the assumption that the grand coalition must form by imposing the axiom of Cohesive efficiency: the total payoffs that the players can share is equal to the maximal total worth generated by a coalition structure. We determine how the three main axiomatic characterizations of the Shapley value are affected when the classical axiom of Efficiency is replaced by Cohesive efficiency. We introduce and characterize two variants of the Shapley value that are compatible with Cohesive efficiency. We show that our approach can also be applied to the variants of more egalitarian values.

Individualism or Collectivism: A Reinforcement Learning Mechanism for Vaccination Decisions

Previous studies have pointed out that it is hard to achieve the level of herd immunity for the population and then effectively stop disease propagation from the perspective of public health, if individuals just make vaccination decisions based on individualism. Individuals in reality often exist in the form of groups and cooperate in or among communities. Meanwhile, society studies have suggested that we cannot ignore the existence and influence of collectivism for studying individuals’ decision-making. Regarding this, we formulate two vaccination strategies: individualistic strategy and collectivist strategy. The former helps individuals taking vaccination action after evaluating their perceived risk and cost of themselves, while the latter focuses on evaluating their contribution to their communities. More significantly, we propose a reinforcement learning mechanism based on policy gradient. Each individual can adaptively pick one of these two strategies after weighing their probabilities with a two-layer neural network whose parameters are dynamically updated with his/her more and more vaccination experience. Experimental results on scale-free networks verify that the reinforcement learning mechanism can effectively improve the vaccine coverage level of communities. Moreover, communities can always get higher total payoffs with fewer costs paid, comparing that of pure individualistic strategy. Such performance mostly stems from individuals’ adaptively picking collectivist strategy. Our study suggests that public health authorities should encourage individuals to make vaccination decisions from the perspective of their local mixed groups. Especially, it is more worthy of noting that individuals with low degrees are more significant as their vaccination behaviors can more sharply improve vaccination coverage of their groups and greatly reduce epidemic size.

Implementation of Subgame-Perfect Cooperative Agreement in an Extensive-Form Game

A novel approach to sustainable cooperation called subgameperfect core (S-P Core) was introduced by P. Chander and M. Wooders in 2020 for n-person extensive-form games with terminal payoffs. This solution concept incorporates both subgame perfection and cooperation incentives and implies certain distribution of the total players' payoff at the terminal node of the cooperative history. We use in the paper an extension of the S-P Core to the class of extensive games with payoffs defined at all nodes of the game tree that is based on designing an appropriate payoff distribution procedure β and its implementation when a game unfolds along the cooperative history. The difference is that in accordance with this so-called β-subgameperfect core the players can redistribute total current payoff at each node in the cooperative path. Moreover, a payoff distribution procedure from the β-S-P Core satisfies a number of good properties such as subgame efficiency, non-negativity and strict balance condition. In the paper, we examine different properties of the β-S-P Core, introduce several refinements of this cooperative solution and provide examples of its implementation in extensive-form games. Finally, we consider an application of the β-S-P Core to the symmetric discrete-time alternating-move model of fishery management.

Feedback Adaptive Learning for Medical and Educational Application Recommendation

Recommending applications (apps) to improve health or educational outcomes requires long-term planning and adaptation based on the user feedback, as it is imperative to recommend the right app at the right time to improve engagement and benefit. We model the challenging task of app recommendation for these specific categories of apps—or alike—using a new reinforcement learning method referred to as episodic multi-armed bandit (eMAB). In eMAB, the learner recommends apps to individual users and observes their interactions with the recommendations on a weekly basis. It then uses this data to maximize the total payoff of all users by learning to recommend specific apps. Since computing the optimal recommendation sequence is intractable, as a benchmark, we define an oracle that sequentially recommends apps to maximize the expected immediate gain. Then, we propose our online learning algorithm, named FeedBack Adaptive Learning (FeedBAL), and prove that its regret with respect to the benchmark increases logarithmically in expectation. We demonstrate the effectiveness of FeedBAL on recommending mental health apps based on data from an app suite and show that it results in a substantial increase in the number of app sessions compared with episodic versions of <inline-formula><tex-math notation="LaTeX">$\epsilon _n$</tex-math></inline-formula> -greedy, Thompson sampling, and collaborative filtering methods.

Coordination of Electricity and Natural Gas Systems: An Incentive-Compatible Mutual Trust Solution

The coordination of electricity and natural gas systems has attracted increasing attention in recent years. This paper studies the coordinated dispatch of the two systems from a multi-agent perspective. First, our study demonstrates that the coordinated dispatch may lead to a lower payoff obtained by the power system, while the total utility of the two systems increases. For this issue, a transfer payment strategy that redistributes the total payoff to realize incentive compatibility is proposed. To extend its calculation to an incomplete information scenario considering the multi-agent premise, a two-stage algorithm is developed to solve the coordinated dispatch problem and calculate the transfer payment in a decentralized manner. In this algorithm, an anti-cheating mechanism is integrated to ensure mutual trust of the two systems. The numerical test results of two cases confirm the issue of power systems that lack economic incentives in coordination without a transfer payment and further validate the proposed incentive-compatible mutual trust mechanism.

Is Kindness the Magical Spell? The Role of Information and Reciprocity in Revenue-sharing Crowdfunding

We consider an entrepreneur funding his project from investors through a revenue-sharing crowdfunding campaign. The early investor (insider) has a social tie with the entrepreneur and is informed about the future revenue of the entrepreneur’s project. The investor who arrives later (outsider) is uninformed about the future revenue of the entrepreneur’s project and makes an investment after observing the insider’s investment. Thus, the insider’s investment decision may signal information regarding the entrepreneur’s future revenue to the outsider. While the outsider’s investment decision is solely financially driven, the insider has reciprocity toward the entrepreneur and considers both her own payoff as well as the entrepreneur’s payoff in making her investment decision. We investigate how reciprocity and information asymmetry play a role in the outcome of the crowdfunding campaign. We find that information asymmetry benefits both investors because the insider may invest less to signal her type, which moderates the competition between investors. In addition, reciprocity can affect the information transmission in the crowdfunding campaign. In particular, if the reciprocity is higher than a threshold, the separating equilibrium may not exist, and the insider cannot signal the future project’s revenue via distorting her investment. Moreover, when the separating equilibrium exists, the investors’ total payoff decreases as the reciprocity increases. This is because the insider invests more as the reciprocity increases, which intensifies the competition and reduces the outsider’s payoff. To respond to the insider’s increase of investment, due to the increase of reciprocity, the outsider invests less when there is no information asymmetry, but will first increase and then decreases his investment when there is information asymmetry. Our results provide guidelines for platforms and entrepreneurs on how to leverage the social network in a crowdfunding campaign.

Common-value group contests with asymmetric information

We study contests between two groups where all the players have a common value of winning. In each group one of the players has an information advantage over the other players. This player is referred to as the dominant player. We show that a group contest is equivalent to a contest between the dominant players, and, as such, the expected total effort of both groups is always the same, while their probabilities of winning as well their expected total payoff are not.

GROSE: Optimal group size estimation for broadcast proxy re-encryption

In this paper, we propose GROSE —a scheme to determine the optimal group size of the receiver in Broadcast Proxy Re-encryption. GROSE uses the Rubinstein–Ståhl bargaining approach, so that both the sender and the receiver are mutually benefited and the total payoff increases. Proxy re-encryption is an important scheme to send cloud data securely to another user. For secured sharing of data to more than one users, the concept of broadcast proxy re-encryption was introduced. However, it poses an overhead on the receiver, if the receiver group is large, as each of the receivers in the group has to calculate the additional expensive algebraic operations, which linearly increases with the number of users in the receiver group. We model the problem using a bargaining perspective, and address it as the Rubinstein–Ståhl bargaining game for finite version of the game and then extend it to the infinite horizon version of it. We define the payoff of the sender and the receiver, and discuss how the payoff depends on different factors. Finally, we implement GROSE and compare it with the previously proposed schemes to depict the efficiency of GROSE. It is observed that GROSE is more efficient than the traditional proxy re-encryption and broadcast proxy re-encryption schemes.

Social preferences and coordination: An experiment

In this paper, we use a laboratory experiment to analyze the effect of social preferences in a coordination game with Pareto-ranked equilibria. Inequality is increased by increasing the coordination payoffs of some subjects while the coordination payoffs of others remain unchanged. Theoretically, in this setting, inequality aversion may lead to a negative relationship between inequality and coordination success, while total payoff motivations lead to a positive relationship. Using a within-subject experimental design, we find that more inequality unambiguously yields a higher level of coordination success. Furthermore, this result holds even for subjects whose payoffs remain unchanged. Our results suggest that total payoff motivations drive the positive relationship between inequality and coordination success found in this experiment. Moreover, our data highlight that the order of treatment matters. Groups facing over time a reduction in inequalities reach the efficient outcome more often, over the entire experiment, compared to groups facing over time an increase in inequalities. This study thus contributes to understanding whether social preferences and variations in inequality affect the outcome of coordination problems.

Interplay of Elementary Interactions Causing Social Traps in Evolutionary Games

In evolutionary games, pair interactions are defined by payoff matrices that can be decomposed into four types of orthogonal elementary games that represent fundamentally different interaction situations. The four classes of elementary interactions are formed by games with self- and cross-dependent payoffs, coordination games, and cyclic games. At the level of two-person games, social traps (dilemmas) can not occur for symmetric payoff matrices, which are combinations of coordination games and symmetrically paired self- and cross-dependent components, because individual and common interests coincide in them. In spatial evolutionary games that follow the logit evolutionary dynamics, however, the total payoff is still not maximized at certain noise levels in certain combinations of symmetric components. This phenomenon is similar to the appearance of partially ordered phases in solid state physics, which are stabilized by their higher entropy. In contrast, it is the antisymmetric part of their self- and cross-dependent components that is responsible for the emergence of traditional social dilemmas in games like the two-strategy donation game or the prisoner's dilemma. The general features of these social dilemmas are inherited by $n$-strategy games in the absence of cyclic components, which would prevent the existence of a potential and thus thermodynamic behavior. Using the mathematical framework of matrix decomposition, we survey the ways in which the interplay of elementary games can lead to a loss of total payoff for a society of selfish players. We describe the general features of different illustrative combinations of elementary games, including a game in which the presence of a cyclic component gives rise to the the tragedy of the commons via a paradoxical effect.

Incentivizing Mobile Video Users With Data Sponsoring and Edge Caching

Recently, edge caching has emerged as a new data sponsoring scheme, where the devices on the edge network, e.g., 4G/5G base stations, cache video contents and directly delivery them to mobile video users. Such caching schemes have significantly decreased the backhaul congestion, reduce the delivery latency, thus attract more mobile video users and monetize a huge number of videos. In this paper, we consider a hybrid caching schemes system, which combines edge cache sponsoring (ECS) and traditional cellular data sponsoring (CDS), where the content providers display advertisements to mitigate the cellular data downloading expense and provide attractive contents for mobile video users. In the system, we investigate the cooperative scenario and the competitive scenario between the two sponsoring schemes, and a mobile video user can select one scheme or neither of them for the requests of a video. In the first scenario, we focus on achieving the maximal total benefit of content providers by optimizing CDS and ECS schemes together. In the second scenario, we separately optimize ECS and CDS schemes and maximize the benefits of corresponding content providers. To illustrate the effectiveness of the two scenarios, we formulate the system as a two-stage game: 1) in the first stage, content providers (i.e., leaders) choose the sponsor effort in sponsor schemes (cooperatively or competitively); 2) in the second stage, mobile video users (i.e., followers) select their sponsor preferences. We conduct numerical results to explore the sub-game perfect equilibrium and find that the joint utilization of the two sponsor schemes can benefit the mobile video users, i.e., when ECS and CDS compete with each other, MUs can benefit 36% ~ 140% more than the case where there exists only one sponsor scheme. While when CPs cooperate with each other, their total payoff is maximized, the mobile video users' payoff also increases. Moreover, we find that ECS can benefit the content providers more than CDS when sponsor revenue is high, and this indicates that ECS is a promising sponsor scheme for the high-value contents.

Multi-Armed Exponential Bandit

Exponential bandits are widely adopted in economics and marketing due to their tractability. This paper analyzes the one-agent multi-armed account of exponential bandits, where the agent dynamically selects arms to maximize total payoff. We motivate our base model by examples with arms being of the same type, while the results are generalized to cases where arms are either independent or dependent. The contribution is fourfold. First, we characterize the optimal policy for the agent to choose arms. Under the optimal policy, the agent selects one arm each time, and an arm is used at most once. Second, we show that the agent may not regard information acquisition as a last-ditch effort before quitting, which contradicts the existing literature. Third, with a discount factor, an arm may be used more than once. Fourth, for the case of negatively correlated bandits, the agent may use more than one arms simultaneously. The paper is of both theoretical and practical significance since the model fits well with various situations, including project selection, product promotion, and drug development. Implications for these applications are discussed.

Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

Wireless data demands keep rising at a fast rate. In 2016, Cisco measured a global mobile data traffic volume of 7.2 Exabytes per month and projected a growth to 49 Exabytes per month in 2021. Wi-Fi plays an important role in this as well. Up to 60% of the total mobile traffic was off-loaded via Wi-Fi (and femtocells) in 2016. This is further expected to increase to 63% in 2021. In this publication, we look into the roll-out of public Wi-Fi networks, public meaning in a public or semi-public place (pubs, restaurants, sport stadiums, etc.). More concretely we look into the collaboration between two parties, a technical party and a venue owner, for the roll-out of a new Wi-Fi network. The technical party is interested in reducing load on its mobile network and generating additional direct revenues, while the venue owner wants to improve the attractiveness of the venue and consequentially generate additional indirect revenues. Three Wi-Fi pricing models are considered: entirely free, slow access with ads or fast access via paid access (freemium), and paid access only (premium). The technical party prefers a premium model with high direct revenues, the venue owner a free/freemium model which is attractive to its customers, meaning both parties have conflicting interests. This conflict has been modeled using non-cooperative game theory incorporating detailed cost and revenue models for all three Wi-Fi pricing models. The initial outcome of the game is a premium Wi-Fi network, which is not the optimal solution from an outsider’s perspective as a freemium network yields highest total payoffs. By introducing an additional compensation scheme which corresponds with negotiation in real life, the outcome of the game is steered toward a freemium solution.

Stability for best experienced payoff dynamics

We study a family of population game dynamics under which each revising agent randomly selects a set of strategies according to a given test-set rule; tests each strategy in this set a fixed number of times, with each play of each strategy being against a newly drawn opponent; and chooses the strategy whose total payoff was highest, breaking ties according to a given tie-breaking rule. These dynamics need not respect dominance and related properties except as the number of trials become large. Strict Nash equilibria are rest points but need not be stable. We provide a variety of sufficient conditions for stability and for instability, and illustrate their use through a range of applications from the literature.

Cooperative Fisheries Outperform Non-cooperative Ones in the Baltic Sea Under Different Climate Scenarios

Game theory has been an effective tool to generate solutions for decision making in fisheries involving multiple countries and fleets. Here, we use a coupled bio-economic model based on a Baltic Sea dynamic multispecies food web model called BALMAR and, we compare non-cooperative (NC) and cooperative game (Grand Coalition: GC) solutions. Applications of game theory based on a food web model under climate change have not been studied before and the present study aims to fill this gap in the literature. The study focuses on the effects of climate variability on the biological, harvest and economic output of the game models by examining two different climate scenarios, a first scenario characterized by low temperature and high salinity and a second scenario by high temperature and low salinity. Our results showed that in the first scenario sprat spawning stock biomass (SSB) and harvest dropped dramatically both in the NC and the GC cases whereas, herring and cod SSBs and harvests were higher compared to a base scenario keeping temperature and salinity at mean historical levels. In the second scenario, the sprat SSB and the harvest was higher for both GC and NC cases while the cod and the herring SSBs and harvests were lower. The total GC payoffs clearly outperformed the NC payoffs across all scenarios. Likewise, the first and second scenario GC payoffs for countries were higher except for Poland. The findings suggested the climate vulnerability of Baltic Sea multi-species fisheries and these results would support future decision-making processes of Baltic Sea fisheries.