Iterative Game Research Articles

Outcomes assessment pitfalls: challenges to quantifying knowledge gain in a sex education game

Background: We describe challenges associated with incorporating knowledge assessment into an educational game on a sensitive topic and discuss possible motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a tool for collecting data about family planning intentions. The game was expanded to include information about human anatomy and sexual reproduction. To assess the efficacy of the game as a tool for teaching sexual education, we designed a pre-post study with assessments before and after the game which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The pre-post process did not effectively assess knowledge gain and made the game less enjoyable. Although all participants completed the pre-test because it was required to access the main game, many did not complete the post test. As a result, the post-test scores are of limited use in assessing the efficacy of the intervention as an educational tool. This deployment demonstrated that pre-post testing has to be integrated in a way that motivates players to improve their scores in the post-test. The pre-test results did provide useful information about players’ knowledge of human anatomy and mechanisms of human reproduction prior to gameplay and validated the tool as a means of data collection. Conclusion: Adding outcomes assessment required asking players questions about sexual anatomy and function with little or no introduction. This process undermined elements of the initial game design and made the process less enjoyable for participants. Understanding these failures has been a vital step in the process of iterative game design. Modifications were made to the pre-post test process for future deployments so that the process of assessment does not diminish enthusiasm for game play or enjoyment and motivates completion of the post-test as part of gameplay.

Strategic voting in the lab: compromise and leader bias behavior

Plurality voting is perhaps the most commonly used way to aggregate the preferences of multiple voters. Yet, there is no consensus on how people vote strategically, even in very simple settings. The purpose of this paper is to provide a comprehensive study of people’s voting behavior in various online settings under the plurality rule. We implemented voting games that replicate two common real-world voting scenarios in controlled experiments. In the first, a single voter votes once after seeing a pre-election poll. In the second game, a group of voters play an iterative game, and change their vote as the game progresses (as in online voting). The winning candidate in each game (and hence the subject’s payment) is determined using the plurality rule. For each of these settings we generated hundreds of game instances, varying conditions such as the number of voters, subjects’ preferences over candidates and the poll information that was made available to the subjects prior to voting. We show that people can be classified into several groups, one of which is not engaged in any strategic behavior, while the largest group demonstrates both a tendency for strategic compromise, and a bias toward voting for the leader in the poll. We provide a detailed analysis of this group behavior for both settings, and how it depends on the poll information. Our study has insight for multi-agent system designers in uncovering patterns that provide reasonable predictions of voters’ behaviors, which may facilitate the design of agents that support people or act autonomously in voting systems.

On profitability of block withholding strategies

We present recent developments in the understanding of the profitability block withholding strategies in Bitcoin mining and other Proof-of-Work based blockchains. Block withholding strategies (like selfish, stubborn, trailing or catch-up mining) are rogue mining strategies that violate the rules of the Bitcoin protocol. The authors found recently the exact model based on iterative games to evaluate the profitability per unit time. With a novel application of martingale techniques and Doob's Stopping Time Theorem we compute their profitability in close-form. We can then compare in parameter space these strategies and honest mining, and decide which one is more profitable.

Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model

This study develops a Stackelberg-Nash game model (SNGM) to capture the interdependence between aircraft routing of airlines and maintenance staffing of maintenance providers, and to consider the price competition among maintenance providers. The SNGM’s overall Nash equilibrium is obtained using an iterative game algorithm. The SNGM effectiveness is demonstrated with a case study, in which a neural network-based algorithm is developed to forecast accurate non-propagated delays, and a multiple linear regression algorithm is adopted to predict demand-price relationship for each maintenance provider. The results reveal cost savings of about 26% and 22% for the airline and the maintenance providers, respectively.

Analyzing Iterative Training Game Design: A Multi-Method Postmortem Analysis of CYCLES Training Center and CYCLES Carnivale

That games can be used to teach specific content has been demonstrated numerous times. However, although specific game features have been conjectured to have an impact on learning outcomes, little empirical research exists on the impact of iterative design on learning outcomes. This article analyzes two games that have been developed to train an adult audience to recognize and avoid relying on six cognitive biases (three per game) in their decision making. The games were developed iteratively and were evaluated through a series of experiments. Although the experimental manipulations did not find a significant impact of the manipulated game features on the learning outcomes, each game iteration proved more successful than its predecessors at training players. Here, we outline a mixed-methods approach to postmortem game design analysis that helps us understand what might account for the improvement across games, and to identify new variables for future experimental training game studies.

Stag hunt and trust emergence in social networks

Trust plays an important role in interactions within complex social systems. In this paper, we use multi-agent learning to study trust emergence in social networks. In our setting, agents play an iterative game of Stag Hunt with their neighbors. An agent adopts the learning rule imitate-the-best to learn how to play the game. Trust emerges if all agents choose to hunt stag after repeated play of the Stag Hunt game. We study the probability of the emergence of trust among agents living in different social networks. Using experiments, we reveal critical points of trust emergence in lattice network, ring network and small world model. The probability of trust emergence is relatively low if the quotient of the value of trust and the basic utility is smaller than the critical point. The probability, however, grows quickly when the quotient is larger than the critical point. In scale-free networks, there is no such critical point. Our findings also demonstrate that on scale-free networks, trust emerges only if the value of trust is several times larger than the basic utility.

Evolving cooperation in spatial population with punishment by using PSO algorithm

Understanding the effects of punishment in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations with punishment. Two kinds of spatial structure are used, regular and random connected network. Key model parameters include the number of players, the interaction topology, the punishment and the cost-to-benefit ratio. The simulation results reveal that the punishment can promote the levels of cooperative behaviors to some extent, the cost-to-benefit ratio and the number of players is important factors in determining the strategy evolution. While punishment improves cooperation in these new environments, the impact of punishment is different in two social dilemma games. The spatial structures add the complex of the evolution and the random connected network can better isolate the defectors.

A Novel Reaction Scheme against the MAC Layer Misbehavior in Mobile Ad Hoc Networks

Medium Access Control (MAC) protocol provides a fair access to the wireless channel through a distributed algorithm called the binary exponential back-off. The share nature of the channel makes networks vulnerable to several attacks like the MAC layer misbehavior. The MAC layer misbehavior can be considered as a denial of service attack, through modifications in the backoff algorithm. Indeed, greedy nodes try to enhance their throughput by choosing a small waiting time at the expanse of honest nodes. A single detection mechanism is inadequate against this type of attack; hence a reaction scheme is needful. In this paper we propose a novel reaction scheme against the MAC layer misbehavior, with a minimum of changes to the IEEE 802.11 protocol. Our reaction is in the form of an iterative and cooperative game played by different honest nodes, those who respect the protocol. We also propose a variant of our reaction scheme based on the polynomial regression, in order to reduce the reaction delay. The performance validation of the so called reaction was done with NS2 simulator. The reaction method that we propose tends to align with the cheater node while keeping the fair aspect of the IEEE802.11 standard.

A Self-Adaptive Strategy for Evolution of Cooperation in Distributed Networks

This paper studies the phenomenon of the evolution of cooperation in distributed networks by using an iterated game. An iterated game in a distributed network is a multiple round game, where in each round, a player gains a payoff by playing a game with its neighbours and updates its action based on the actions and/or payoffs of its neighbours. The interaction model between players is usually represented as a two-player, two-action (namely cooperation and defection) Prisoner’s Dilemma game (which is a prototypical model for interaction between selfish individuals). Many researchers have developed strategies (also called update rules) for the evolution of cooperation in distributed networks in order to enhance cooperation, i.e., to increase the proportion of cooperators. Experimental results reported in the current literature, however, have demonstrated that each of these strategies has both advantages and disadvantages. In this paper, a self-adaptive strategy is proposed for the evolution of cooperation in distributed networks, which can utilise the strengths and avoid the limitations of existing strategies. Moreover, we have a theoretical finding about the final proportion of cooperators, evolved by any pure (or deterministic) strategies, in four types of a game. This finding is independent of the initial proportion of cooperators, the topology of the network (e.g., a small-world network or a scale-free network), and the specific game (e.g., the Prisoner’s Dilemma game or the Snow Drift game).

While Enacting Computational Algorithmic Thinking in the Context of Game Design

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that it solves the problem, and evaluating the solution based on some set of criteria. CAT has roots in Mathematics, through problem solving and algorithmic thinking. CAT lies at the heart of Computer Science, which is defined as the study of algorithms. CAT embodies the ability to think critically and creatively to solve problems and has applicability in a range of areas from Computer Science to cooking to music. This article introduces CAT as explored through the Supporting Computational Algorithmic Thinking (SCAT) project, an on-going longitudinal between-subjects research project and enrichment program that guides AfricanAmerican middle school girls (SCAT Scholars) through the iterative game design cycle resulting in a set of complex games around broad themes. This article also explores the difficulties SCAT Scholars face while using CAT capabilities in the context of game design over almost two years as described by the Scholars themselves in online journals.

Iterated crowdsourcing dilemma game

The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to address this question. We enumerate pure evolutionarily stable strategies within the class of so-called reactive strategies, i.e., those depending on the last action of the opponent. Among the 4096 possible reactive strategies, we find 16 strategies each of which is stable in some parameter regions. Repeated encounters of the players can improve social welfare when the damage inflicted by an attack and the cost of attack are both small. Under the current framework, repeated interactions do not really ameliorate the crowdsourcing dilemma in a majority of the parameter space.

On Ibn Ezra's Procedure and Shapley Value

We examine ibn Ezra's procedure (Rabinovitch 1973; O'Neill 1982) historically used to solve the Rights Arbitration problem in the general framework of bankruptcy problems. When the greatest claim is larger than or equal to the estate, the procedure is a maximal game (Aumann 2010). However, when the greatest claim is smaller than the estate, the axioms of efficiency (the whole estate is distributed) and satiation are difficult to satisfy simultaneously. We discuss both axioms to show that their importance and necessity are radically different. From then, for the part of the estate not covered by the greatest claim, we examine four possible procedures: the minimal overlap rule, Alcalde et al.'s (2005) iterative generalization, dictator game, and unanimity game. They pose many problems mainly because: (i) only the greatest claimant remains in the game; (ii) playing two different subgames is unrealistic; (iii) some claimants may receive more than they claim, which violates the satiation axiom; (iv) under the minimal overlap rule players simultaneously play a unanimity game and a dictator game; (v) Alcalde et al.'s generalization is is efficient but converges very slowly. This is why we propose an original and different rule, the series of nested unanimity games (SNUG). We show that it is guarantees efficiency and satiation for all claimants and compared to the other rules, SNUG is attractively homogenous --- only unanimity games are played.

Developing game-like simulations to formalize tacit procedural knowledge: the ONT experience

The exceptional success achieved by the Spanish National Transplant Organization (ONT) in recent years has made other countries highly interested in following this organization’s methodologies. A good training program is one of the key elements of the ONT. Until 2012, the ONT training program was a paper-based case teaching method, and the small number of ONT experts limited the audience. In an attempt to improve and increase the attendees in this program, a game-like simulation was developed to represent transplant management procedural knowledge. To maximize the educational value, this game-like simulation was based on representative teaching cases to help students practice with different real situations and different levels of complexity in a risk-free environment. This study presents how an iterative game development methodology has been applied to evolve from a paper-based case teaching method to a game-like simulation, with a special focus on the efforts made to include the ONT experts’ tacit procedural knowledge in the simulation. Apart from increasing the number of students who can access the ONT training, this game-like simulation also helped to achieve a more detailed formalization of transplant management as well as a more comprehensive systematization of a set of relevant teaching cases.

Cooperation and depressive symptoms

BackgroundDeficits in pro-social cooperation are common in many individuals with mental illnesses such as depression. For decades, researchers have used economic game paradigms to compare cross-cultural cooperative behavior. However, research using economic games to assess cooperative behavior in clinical populations is in the early stages. We hypothesized that individuals with greater depressive symptoms would struggle to maintain reciprocity in iterative games, but not in single-iteration games measuring personal values. MethodsParticipants (n=41) played four computer-based economic games (prisoner's dilemma, the public goods game, the ultimatum game, and the trust game) measuring different aspects of cooperation. Participants completed the Depression Anxiety and Stress Scale (DASS) and other measures of personality and demographics. Analyses assessed the relationships between game performance and psychological distress as measured by the DASS. ResultsSignificant correlations were found between game performance and depressive symptoms, but not symptoms of anxiety or stress. Performance in the prisoner's dilemma and public goods game was significantly related to depression in a linear regression even when known associations with depressive affect such as age, gender, race, education, marital status, and neuroticism were controlled for. ConclusionsDepressive symptoms were associated with an inability to sustain reciprocal cooperation. Participants showed the predicted deficits in cooperation in these economic games. Economic games show the potential for assessing the social deficits associated with depressive symptoms.

Designing Automated Allocation Mechanisms for Service Procurement of Imperfectly Substitutable Services

Self-serving assets (SSAs) are a new interpretation of the intelligent product technology, set to transform product lifecycle management through automation. SSAs are engineering assets that autonomously monitor their health and expiry dates, search for suppliers, and negotiate with them, while they are still in use by the customer. The concept enables more timely and transparent supplier decision making while eliminating central database transactions and tedious manual effort. Autonomous self-interested agents that act on behalf of their stakeholders naturally give rise to an allocation problem, under the assumption of private information held by trade parties and capacity constrained suppliers providing imperfectly substitutable goods (ISGs). In this paper, we develop and compare three automated competition mechanisms, constructed as iterative games, and test them in the context of the aerospace service supply chain. The competition mechanisms include a prioritized selection mechanism, extended Vickrey, and reverse Dutch auctions. Our context drives us to seek mechanisms that will not only perform well in terms of economic theory, but also in terms of computational performance. Key findings are that extended Vickrey auctions can handle multiple criteria and provide higher market efficiency at lower computational cost, especially in small to medium markets. As scalability is an issue in large markets, the use of auctions is recommended only for complex high value assets or under uncertain market scenarios. As business-to-business (B2B) environments are becoming the norm for many global companies, our study aims to be exemplary to those who would like to implement automated auction mechanisms in highly complex environments.

Modeling Learning and Cooperation in Iterative Games

In this paper, we describe the general framework of neural networks and how such frameworks can be adapted to model human game play and the learning that takes place during iterative games. We introduce a method of pre-processing game matrices in an effort to produce cooperative strategies in games with non-cooperative dominant strategies, such as the Nash Equilibrium solution to the Prisoner’s Dilemma. We find that the introduction of the pre-processed matrix increases the probability that the network plays a cooperative strategy significantly when compared to the network behavior without pre-processing.

Why Just Two Parties? A Voting Game to Illustrate Duverger's Law

AbstractWith the 2012 presidential elections approaching, student interest in (and frustration with) the US two-party system is likely to grow. Although many explanations exist for this phenomenon, their number and complexity often overwhelm students. This article describes an iterative game, designed for a single class session, that illustrates one of the major institutional explanations for the US two-party system—Duverger's Law. The game can also spur discussion of some of the major criticisms of the Law.

Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games

Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoner's Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.

Convergence of utility-based power control in Gaussian interference channel

The convergence of non-cooperative distributed power control in Gaussian interference channel is analysed in this study. Firstly, the existing distributed power control schemes are categorised as two types: gradient projection type and non-linear type, according to the iterative steps. A unified mathematical formulation is then provided for each type. The objective is extended from rate maximisation, which has been studied a lot in iterative water-filling game, to more general form of utility maximisation. Based on it, a set of sufficient conditions are derived for each type of the schemes, guaranteeing the uniqueness of the fixed point and the global convergence in a totally asynchronous manner. These convergence requirements can be interpreted as different degree of separation, according to the concrete form of utility function. To get insight into the derived convergence conditions, some numerical results are presented in the end.

Distributed Coding of Actual and Hypothetical Outcomes in the Orbital and Dorsolateral Prefrontal Cortex

Knowledge about hypothetical outcomes from unchosen actions is beneficial only when such outcomes can be correctly attributed to specific actions. Here we show that during a simulated rock-paper-scissors game, rhesus monkeys can adjust their choice behaviors according to both actual and hypothetical outcomes from their chosen and unchosen actions, respectively. In addition, neurons in both dorsolateral prefrontal cortex and orbitofrontal cortex encoded the signals related to actual and hypothetical outcomes immediately after they were revealed to the animal. Moreover, compared to the neurons in the orbitofrontal cortex, those in the dorsolateral prefrontal cortex were more likely to change their activity according to the hypothetical outcomes from specific actions. Conjunctive and parallel coding of multiple actions and their outcomes in the prefrontal cortex might enhance the efficiency of reinforcement learning and also contribute to their context-dependent memory.