Higher Payoff Research Articles

Coordination with Differential Time Preferences: Experimental Evidence

The experimental literature on repeated games has largely focused on settings where players discount the future identically. In applications, however, interactions often occur between players whose time preferences differ. We study experimentally the effects of discounting differentials in infinitely repeated coordination games. In our data, differential discount factors play two roles. First, they provide a coordination anchor: more impatient players get higher payoffs first. Introducing even small discounting differentials reduces coordination failures significantly. Second, with pronounced discounting differentials, intertemporal trades are prevalent: impatient players get higher payoffs for an initial phase and patient players get higher payoffs in perpetuity afterward. (JEL C72, C92, D15, D25)

Evolutionary origins of temporal discounting: Modeling how time and uncertainty constrain optimal decision-making strategies across taxa.

The propensity of humans and non-human animals to discount future returns for short-term benefits is well established. This contrasts with the ability of organisms to unfold complex developmental sequences over months or years efficiently. Research has focused on various descriptive and predictive parameters of 'temporal discounting' in behavior, and researchers have proposed models to explain temporal preference in terms of fitness-maximizing outcomes. Still, the underlying ultimate cause of this phenomenon has not been deeply explored across taxa. Here, we propose an ultimate (i.e., evolutionary) causal explanation for the selection of temporal discounting largely conserved across taxa. We propose that preference for a short-term reward (e.g., heightened impulsivity) often is less than optimal and likely is the product of constraints imposed on natural selection with respect to predicting events in a temporal framework in the context of future uncertainty. Using a simple Newtonian model for time across a fitness landscape in which movement by organisms is only possible in one direction, we examine several factors that influence the ability of an organism to choose a distant reward over a more temporally proximate reward: including the temporal distance of the far reward, the relative value of the distant reward, and the effect of uncertainty about the value and presence of the distant reward. Our results indicate that an organism may choose a more distant reward, but only if it is not too far into the future and has a substantially higher-value fitness payoff relative to the short-term reward. Notably, any uncertainty about the distant reward made it extremely unlikely for an organism to choose the delayed reward strategy compared to choosing a closer reward, even if the distant reward had a much higher payoff because events that are uncertain are only partially visible to natural selection pressures. The results help explain why natural selection is constrained to promote more optimal behavioral strategies and why it has difficulty selecting a distant reward over a lower-value short-term reward. The degree of uncertainty is an especially salient ecological variable in promoting and preferencing short-term behavioral strategies across taxa. These results further help illustrate why, from an ultimate causal perspective, human and non-human taxa have difficulty making more optimal long-term decisions.

Linking Spontaneous Behavioral Changes to Disease Transmission Dynamics: Behavior Change Includes Periodic Oscillation.

Behavior change significantly influences the transmission of diseases during outbreaks. To incorporate spontaneous preventive measures, we propose a model that integrates behavior change with disease transmission. The model represents behavior change through an imitation process, wherein players exclusively adopt the behavior associated with higher payoff. We find that relying solely on spontaneous behavior change is insufficient for eradicating the disease. The dynamics of behavior change are contingent on the basic reproduction number corresponding to the scenario where all players adopt non-pharmaceutical interventions (NPIs). When , partial adherence to NPIs remains consistently feasible. We can ensure that the disease stays at a low level or maintains minor fluctuations around a lower value by increasing sensitivity to perceived infection. In cases where oscillations occur, a further reduction in the maximum prevalence of infection over a cycle can be achieved by increasing the rate of behavior change. When , almost all players consistently adopt NPIs if they are highly sensitive to perceived infection. Further consideration of saturated recovery leads to saddle-node homoclinic and Bogdanov-Takens bifurcations, emphasizing the adverse impact of limited medical resources on controlling the scale of infection. Finally, we parameterize our model with COVID-19 data and Tokyo subway ridership, enabling us to illustrate the disease spread co-evolving with behavior change dynamics. We further demonstrate that an increase in sensitivity to perceived infection can accelerate the peak time and reduce the peak size of infection prevalence in the initial wave.

Coordinated and uncoordinated punishment in a team investment game

Coordinated punishment occurs when punishment requires a specific number of punishers to be effective, otherwise, no damage will be inflicted on the target. While societies often rely on this punishment device, its benefits are unclear compared to uncoordinated punishment, where punishment decisions are substitutes. In this paper, we compare the efficacy of coordinated and uncoordinated punishment in a team investment game with two investors and one allocator. Our findings indicate that coordinated punishment results in higher levels of cooperation and reciprocity, as measured by the levels of joint investment and the return by allocators. Importantly, this does not translate into higher payoffs: investors use punishment more frequently when this is coordinated, which destroys the efficiency gains generated by the highest investment. In fact, our results suggest that the highest level of efficiency would be achieved if investors were not allowed to punish.

Explaining mobilization for revolts by private interests and kinship relations.

Mobilization for revolts poses a significant challenge for rational choice theory because revolts are vulnerable to free-riding, which disincentivizes rational actors from mobilizing. Strong, informal relations such as kinship ties have been identified as factors that can shift the rational calculations of individuals and lead to mobilization for revolts. In social networks that are polarized by the presence of mobilized individuals, such as rebels, and actors opposing the mobilization effort such as the elite, kinship relations have not only a bridging effect but also a diverging one. Building on Tullock's private interest theory, we develop a framework in which kinship relations determine the extent of individual's payoffs and costs of mobilization for revolts against an elite. We posit that distant kin of the elite expect high payoffs of mobilization for revolts and face the lowest costs of mobilization for revolts by virtue of their position in the network of kinship relations. Using a unique, hand-collected dataset that reconstructs a revolt in Basel, Switzerland, in 1691, we test our framework and contribute to a better relational understanding of the mechanisms that lead rational actors to mobilize for revolts. Our analyses show that mobilization for revolts is mainly driven by distant kinship relations to the ruling elite rather than close kinship relations to the rebels.

Going green: Framing effects in a dynamic coordination game

We study decision-making in a (novel) dynamic coordination game that simulates ‘social tipping’. The game involves groups of three subjects who initially find themselves in a stable state with positive network externalities. Over seven rounds, they have the opportunity to transition to an alternative stable state with higher payoff, based on the other network. The transition is slow, costly and requires coordination to minimize costs.In the experiment, the game is repeated five times, which enables groups to learn to coordinate. We compare a neutral language treatment with a ‘green framing’ treatment, in which meaningful context is added to the instructions. Our findings reveal that green framing significantly increases the number of successful transitions, but also reduces the group's ability to coordinate in subsequent games. Consequently, while green framing results in twice as many transitions, these are also more costly such that payoffs do not differ between treatments.In the context of environmental policy, the experiment suggests general support for ‘going green’, but it also points to green framing causing proponents and opponents to stick to their strategies reducing potential gains from coordination between proponents and opponents of environmentally friendly actions.

A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment

Abstract We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a healthcare technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the healthcare system. The framework takes into account the cost of decision errors in the payoff function, allowing the decision maker to compute the cost of taking a decision when evidence is far from the optimal decision triggers. We show, using a real-world cost-effectiveness study based on clinical trial evidence, how rules derived from a sequential adaptive design approach can lead to quicker decisions when compared to the value of information decision framework. Our application shows that a sequential approach has the potential to lead to quicker decisions, higher payoffs, and better health outcomes.

Urban Regeneration through Integrated Strategies to Tackle Inequalities and Ecological Transition: An Experimental Approach

Cities have a growing role to play in ensuring environmental transition while addressing issues of social inclusion and social sustainability. The aim of this study was to evaluate a multidimensional policy of the city of Messina, in southern Italy, aimed at experimenting a sustainable model of urban regeneration to overcome its shantytowns after the 1908 earthquake. The impact indicators of a pilot program of urban generation were collected in terms of environmental, social, and economic criteria, while the real experiment focused on the mechanisms of choice related to the housing of extremely marginalized people. In the end, two slums were demolished, and 205 households had a home without additional land consumption. Attitudes toward social cohesion were measured through 29 interviews and the administration of a pre–post structured test. According to the individual payoffs over time, it is possible to correlate housing choice mechanisms with components not related to short-term economic rationality. The development of a positive attitude toward the future and trust in others are associated with the development of the riskiest option, which is also the one that can have the highest payoff. The program strategy reduced reliance on social welfare measures and enforced institutional capacity building and skills for sustainable urban development.

Evolutionarily rational mutations in structured populations

This paper considers an evolutionary dynamic to study games played in large populations that consist of many identical, potentially small subpopulations, where individuals interact with others in their subpopulation, rarely moving between them.Since long-run behavior depends on the used mutation strategies, a criterion loosely inspired by Maynard Smith's (1972) notion of an evolutionary stable strategy is imposed on mutation strategies – we ask which mutation strategies are robust to “invasions” by others. Non-degenerate mutation strategies lead to behavioral diversity. Populations that use different interaction strategies in different subpopulations can be robust to “invasions,” even if each interaction strategy would not be robust if used by the entire population.Mutation strategies satisfying the proposed criterion always exist. A simple characterization is possible for the case of weak selection. The developed theory yields a rich class of predictions that can be inconsistent with standard concepts like Nash equilibrium. For instance, for finitely repeated games, the model always predicts relatively high payoffs if the stage game is repeated sufficiently often.

Epidemic population games and evolutionary dynamics

We propose a system theoretic approach to select and stabilize the endemic equilibrium of an SIRS epidemic model in which the decisions of a population of strategically interacting agents determine the transmission rate. Specifically, the population’s agents recurrently revise their choices out of a set of strategies that impact to varying levels the transmission rate. A payoff vector quantifying the incentives provided by a planner for each strategy, after deducting the strategies’ intrinsic costs, influences the revision process. An evolutionary dynamics model captures the population’s preferences in the revision process by specifying as a function of the payoff vector the rates at which the agents’ choices flow toward strategies with higher payoffs. Our main result is a dynamic payoff mechanism that is guaranteed to steer the epidemic variables (via incentives to the population) to the endemic equilibrium with the smallest infectious fraction, subject to cost constraints. We use a Lyapunov function not only to establish convergence but also to obtain an (anytime) upper bound for the peak size of the population’s infectious portion.

Pareto efficiency as alternative to Nash equilibrium in competitive discrete location under delivered pricing

We address the facility location problem for firms that sell an homogeneous product and compete on delivered prices. If the firms set equilibrium prices at each market, the problem can be seen as a location game for which there exists a Nash equilibrium if demand is inelastic. However, this equilibrium may be Pareto-inefficient and the firms could decide to collude if they can guarantee themselves higher payoffs than those prescribed by the equilibrium. The aim of this paper is to study the joint profit maximization problem to obtain a collusive solution of the location game (Pareto-efficient) and determine if such a solution is also a Nash equilibrium. We develop Integer Linear Programming models to find Pareto-efficient solutions for multiple and single facility location. Nash equilibria are obtained by the best response procedure. An empirical investigation is performed to compare the joint profit of the firms and the profit of each one of the firms, got through the Pareto-efficient locations, with those got through the Nash equilibrium.

Cumulative advantage is a double-edge sword for cooperation

The Matthew effect emphasizes the influence of early advantage on shaping long-term development by amplifying it over time, and its implications for public cooperation are yet to be fully understood. In this letter, we propose and study a spatial public goods game driven by cumulative advantage, where players who achieve high payoffs in a given round receive greater allocations in the next. The simulation results show that the Matthew effect leads to an irreversible polarization of individual wealth on the network. Such polarization makes moderate cooperation levels more prevalent, which helps to explain the widespread coexistence of prosocial and antisocial behavior. Meanwhile, heterogeneous networks may restrict the polarization of wealth, but also inhibit the evolution of cooperation, requiring a reconsideration of the commonly held view that heterogeneous networks enhance cooperation.

A Representation for Many Player Generalized Divide the Dollar Games

Divide the dollar is a simplified version of a two player bargaining problem game devised by John Nash. The generalized divide the dollar game has n>2 players. Evolutionary algorithms can be used to evolve individual players for this generalized game but representation—i.e., a genome plus a move or search operator(s)—must be carefully chosen since it affects the search process. This paper proposes an entirely new representation called a demand matrix. Each individual in the evolving population now represents a collection of n players rather than just an individual player. Players use previous outcomes to decide their choices (bids) in the current round. The representation scales linearly with the number of players and the move operator is a variant of an evolution strategy. The results indicate that this proposed representation for the generalized divide the dollar game permits the efficient evolution of large player populations with high payoffs and fair demand sets.

Harmony or Involution: Game Inspiring Age-of-Information Optimization for Edge Data Gathering in Internet of Things

Age-of-Information (AoI) has been recently reckoned as a suitable parameter to evaluate the freshness of collected information, which is essential for data retrieval in Internet of Things, especially the monitoring tasks, e.g., the operating situation of equipments. To motivate a large number of sensor nodes and solicit more up-to-date information from these nodes, the control center usually allocates rewards to nodes according to their proportional contributions. This induces intense competitions among nodes who try to gain high payoffs by carefully balancing the rewards and the costs. In this article, we propose a novel stochastic game model to formulate the competition among sensor nodes, which considers AoI as a metric used by the control center to quantify the contributions of nodes. We also take into account the uncertainty of channel quality, which affects the transmission success ratio of packets generated by nodes. Finally, we design an ϵ-Nash learning algorithm, which adopts the θ-greedy exploration strategy, to derive the ϵ-approximate Nash equilibrium such that nodes can maximize their long-term payoffs. Our substantive simulation results and analysis verify that the proposed algorithm outperforms baseline algorithms in bringing higher payoffs to nodes and more fresh information to the control center.

The analogical foundations of cooperation

We offer an approach to cooperation in repeated games of private monitoring in which players construct models of their opponents' behavior by observing the frequencies of play in a record of past plays of the game in which actions but not signals are recorded. Players construct models of their opponent's behavior by grouping the histories in the record into a relatively small number of analogy classes for which they estimate probabilities of cooperation. The incomplete record and the limited number of analogy classes lead to misspecified models that provide the incentives to cooperate. We provide conditions for the existence of equilibria supporting cooperation and equilibria supporting high payoffs for some nontrivial analogy partitions.

Combining Conformist and Payoff Bias in Cultural Evolution : An Integrated Model for Human Decision-Making.

Most research on transmission biases in cultural evolution has treated different biases as distinct strategies. Here I present a model that combines both frequency dependent bias (including conformist bias) and payoff bias in a single decision-making calculus and show that such an integrated learning strategy may be superior to relying on either bias alone. Natural selection may operate on humans' relative dependence on frequency and payoff information, but both are likely to contribute to the spread of variants with high payoffs. Importantly, the magnitude of conformist bias affects the evolutionary dynamics, and I show that an intermediate level of conformity may be most adaptive and may spontaneously evolve as it resists the invasion of low-payoff variants yet enables the fixation of high-payoff variants in the population.

Characterizing Agent Behavior in Revision Games with Uncertain Deadline

Revision game is a very recent advance in dynamic game theory and it can be used to analyze the trading in the pre-opening stock market. In such games, players prepare actions that will be implemented at a given deadline, before which they may have opportunities to revise actions. For the first time, we study the role of the deadline in revision games, which is the core component that distinguishes revision games from classic games. We introduce the deadline distribution into revision game model and characterize the sufficient and necessary condition for players’ strategies to constitute an equilibrium. The equilibrium strategy with respect to the deadline uncertainty is given by a simple differential equation set. Governed by this differential equation set, players initially fully cooperate, and the cooperation level decreases as time progresses. The uncertainty has a great impact on players’ behavior. As the uncertainty increases, players become more risk averse, in the sense that they prefer lower mutual cooperation rate rather than higher payoff with higher uncertainty. Specifically, they will not stay in full cooperation for a long time, while after they deviate from the full cooperation, they adjust their plans more slowly and cautiously. The deadline uncertainty can improve the competition and avoid collusion in games, which could be utilized for auction design and pre-opening stock market regulations.

Does size matter in group decision making? Simulation experiments with LNG professionals bidding in auction markets

Purpose: An important issue in decision-making processes is whether groups decide better than individuals. This paper compares the bidding behavior of groups of professionals while playing a business game that simulates, in a controlled environment, the sequential unit capacity auctions in the Spanish LNG market.Design/methodology/approach: First, we randomly grouped professionals in groups of different size—SOLOs, DUOs, and TRIOs—and played the game in-situ under both First and Second price unit capacity auctions, with SOLOs outperforming groups. Second, we ran non-parametric simulations mixing professionals in groups of different size, in which bids were coupled with those registered during the in-situ sessions. Third, we ran non-parametric simulations in which the players were either ‘rational machines’ that bid according to Nash equilibrium or groups of ‘professionals’ of different size.Findings: The size of the decision group does matter. After the in-situ and the bootstrapped simulated games, the main result is that size does matter, and groups are not necessarily superior to individuals bidding alone. SOLO players bid closer to MACHINEs and lower than DUOs or TRIOs, while obtaining about the same number of units and higher payoffs than groups. Additionally, the ‘degree of rationality’ of the participants does also matter.Research limitations/implications: Even after applying the hybrid simulation methodology to increase sample size and allow for additional experimental settings, some of the scenarios are fictitious. Modification of the business game to allow for an even more realistic game could be implemented.Practical implications: After the hybrid simulation approach, the main implication of the paper is that to increase efficiency in resource allocation professionals should bid individually while using the theoretical knowledge of rational machines.Originality/value: To our knowledge, this is the first time that this double-experiment simulation methodology is used to analyze bidding behavior in auctions.

Do Income Stocks Provide Higher Total Return than Growth Stocks? Empirical Data Analysis

The fundamental issue in the investment process is not only how much to invest but also, what stocks to invest. It takes an average investor to make fundamental and/or technical analysis to select the best stock in the market. Of course, the selection dilemma does not lie with which company pays the highest dividends. It lies withwhich stock type (growth or income), provides highest payoff (total return) in the long run. The purpose of this study is to establish whether or not income stocks outperform growth stocks over time, and to offer valuable insights to new and existing investors as towhich path to take when it comes to stock selection. The study takes an empirical approach of analyzing existing data in order to make conceptual contribution to the body of knowledge in the literature of investment finance.In fact, the widespread rally behind dividend-paying stocks is not just a fad but has been supported by empirical evidence (data). Generally, growth stocks provide lesser total returns to the investor than income stocks. A test of mean differences of yields between income and growth stocks saw the yield (4.0) of income stocks almost doubling the yield (2.2) of growth stocks. Analysis of the data further indicatesthat dividends have played significant role in increasing total return in the history of investment. This was evident when average annual equity returns have been lower than 10% during a decade. Further analysis of data also, revealed the consequences of different dividend-policy implementations. The study established that dividend growers and initiators have historically achieved higher total return with less volatility than companies that either maintained or cut their dividend payment. Furthermore, dividend yield appeared to be significant in selecting stocks. Surprisingly, the study found that Stocks in the highest quintile of dividend yields have performed less, relative to stocks in the second quintile over time.

Memory retrieval and harshness of conflict in the hawk–dove game

We study the long-run dynamics of a repeated non-symmetric hawk–dove type interaction between agents of two different populations. Agents choose a strategy based on their previous experience with the other population by sampling from a collective memory of past interactions. We assume that the sample size differs between populations and define a measure of harshness of conflict in the hawk–dove interaction. We then show how the properties of the long-run equilibrium depend on the harshness of conflict and the relative length of the sample. In symmetric interactions, if conflict is harsh, the population which samples relatively more past interactions is able to appropriate a higher payoff in the long-run, while the population with a relatively smaller sample does so if conflict is mild. These results hold subject to constraints on the sample size which we discuss in detail. We further extend our results to non-symmetric hawk–dove games.