Choices Of Players Research Articles

Playing Conspiracy

Playing Conspiracy

Pupil Correlates of Decision Variables in Mice Playing a Competitive Mixed-Strategy Game.

In a competitive game involving an animal and an opponent, the outcome is contingent on the choices of both players. To succeed, the animal must continually adapt to competitive pressure, or else risk being exploited and lose out on rewards. In this study, we demonstrate that head-fixed male mice can be trained to play the iterative competitive game "matching pennies" against a virtual computer opponent. We find that the animals' performance is well described by a hybrid computational model that includes Q-learning and choice kernels. Comparing between matching pennies and a non-competitive two-armed bandit task, we show that the tasks encourage animals to operate at different regimes of reinforcement learning. To understand the involvement of neuromodulatory mechanisms, we measure fluctuations in pupil size and use multiple linear regression to relate the trial-by-trial transient pupil responses to decision-related variables. The analysis reveals that pupil responses are modulated by observable variables, including choice and outcome, as well as latent variables for value updating, but not action selection. Collectively, these results establish a paradigm for studying competitive decision-making in head-fixed mice and provide insights into the role of arousal-linked neuromodulation in the decision process.

Equilibrium player choices in team contests with multiple pairwise battles

We consider games in which team leaders strategically choose the order of players sent to the battlefield in majoritarian team contests with multiple pairwise battles as in Fu et al. (2015). We consider one-shot order-choice games and battle-by-battle sequential player choice games. We show that as long as the number of players on each team is the same as the number of battles, the equilibrium winning probability of a team and the ex ante expected effort of each player in a multi-battle contest are independent of whether players' assignments are one-shot or battle-by-battle sequential. This equilibrium winning probability and ex ante expected total effort coincide with those where the player matching is chosen totally randomly with an equal probability lottery by the contest organizer. Finally, we show how player choices add subtleties to the equivalence result by examples.

NurseVR: Development of a Serious Virtual Reality Game for Medication Preparation and Administration Training.

The use of simulation for nursing education has several advantages. The present study aimed to develop and validate a serious virtual reality game for medication preparation and administration training. This is a methodological study in which a serious virtual reality game named NurseVR was developed and evaluated by teachers and students of a nursing course. In NurseVR, the player presents himself or herself in a simulated hospital environment to prepare and administer a medication. From then on, all the steps to be taken are the player's free choice according to what was learned in the classroom, making the game the closest to reality. High levels of positive agreement to statements concerning the validity of the game were found between students and teachers. The virtual immersion proposal was an innovative tool, and the validation results of the final version of NurseVR allow its use for nursing training.

Goal Recognition with Markov Logic Networks for Player-Adaptive Games

Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital games, which dynamically augment game events in response to player choices for a range of applications, including entertainment, training, and education. However, digital games pose significant challenges for goal recognition, such as exploratory actions and ill-defined goals. This paper presents a goal recognition framework based on Markov logic networks (MLNs). The model’s parameters are directly learned from a corpus that was collected from player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with exploratory actions and ill-defined goals.

“Liberty for Androids!”: Player Choice, Politics, and Populism in Detroit: Become Human

In this article, I analyze the 2018 video game Detroit: Become Human as a potentially political text from the perspective of literary and cultural studies. I argue that it features and presents narrative choices in a way that encourages players to make decisions not solely for personal or empathetic reasons but also through a political contemplation, and I contend that the manner in which the game narratively presents individual agency and populist imaginations of “the people” complicates this political project. To do so, I first present an approach to narrative, agency, and politics in video games more generally, before then discussing questions of agency and politics in Detroit: Become Human on three levels: in its narrative presentation, in how the gameplay politicizes player choice, and in how both the narrative and the ludic elements in the game complicate its interest in politics. This contribution thus suggests ways of both studying the connections of agency and politics in video games and culturally contextualizing this particular way of representing (a)political choices.

Bayesian Clustering of Player Styles for Multiplayer Games

With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player’s choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method which has several advantages: the number of clusters do not need to be specified a priori; the technique can work with a very compact representation of each match (e.g. consisting primarily of indicator variables for player choices); a player can belong to multiple clusters and hence can have a hybrid style; and the resulting clusterings often have a straight-forward interpretation. To demonstrate the approach, we apply our method to multiplayer match logs from Battlefield 3 consisting of over 1200 players and 500,000 matches.

Information theory and player archetype choice in Hearthstone

Using three years of game data of the online collectible card game Hearthstone, we analyse the evolution of the game’s system over the period 2016–2019. By considering the frequencies that archetypes are played, and their corresponding win-rates, we are able to provide narratives of the system-wide changes that have occurred over time, and player reactions to them. Applying the archetype frequencies to analyse the system’s Shannon entropy, we characterise the salient features of the time series of player choice. Paying particular attention to how entropy is affected during periods of both small and large-scale change, we are able to demonstrate the effects of increased player experimentation before popular decks and tactics emerge. Furthermore, constructing conditional probabilities that simulate understandable player behaviour, we analyse the system’s information storage and test the explain-ability of current player choice based on previous decision-making.

Research on the Evolution of the Express Packaging Recycling Strategy, Considering Government Subsidies and Synergy Benefits.

With the year-on-year growth of e-commerce transactions and the increasing popularity of the concept of ecological civilization, the waste and recycling of express packages have aroused widespread discussion and attention. On the issue of express package recycling, how consumers, e-commerce enterprises, and e-commerce platforms choose their own strategies, how to better promote the recycling of express packages, and what is the effect mechanism of government subsidies on different players. These are the questions that this article wants to answer. Since this article involves many stakeholders, in order to better identify the strategic choice and evolution of different entities and to better study the influence of government subsidies on the strategic choice of game players, this article uses two triparty evolutionary game models. The results show that without subsidies, changes in the rate of return and the initial probability will affect the evolution of the equilibrium strategy, while the synergistic benefits will have a corrective effect in some cases; when government subsidies are included and the probability of the three parties choosing “green strategies” is relatively low, subsidies should be paid to e-commerce companies mainly; lower subsidies can only provide incentives for e-commerce platforms. This article can provide certain references and value for government policymakers.

Tripartite Evolutionary Game Analysis for Rumor Spreading on Weibo Based on MA-PT

With the rapid development of social media, Weibo rumors have increasingly attracted widespread attention. To maintain sustainable development of network ecology, it is necessary to clarify the evolution law of rumor propagation. First, evolutionary game model is applied to examine the interaction mechanisms of complex behaviors between Weibo rumor makers, Weibo users and governments in accordance with MA-PT (Mental Accounts and Prospect Theory). Next, the replication dynamic equation is used to solve the equilibrium strategy points of each player. It was found that the evolution game does not have a stable equilibrium point. Finally, we conduct empirical simulation to verify the model and primary conclusions. Results demonstrate that, 1) The reference value plays an important role in the strategic choices of players. 2) With the high value of psychological satisfaction of “Herding effect,” Weibo users choose to repost rumors. 3) Increasing the value of punishment, Weibo rumor makers are less likely to adopt the strategy of making rumors. 4) The higher loss of social unrest can encourage governments to adopt strict management strategies to maintain good online order. The results can guide governments to make better decisions in network governance.

Beyond hours of video gameplay: Connections between verbal aggressiveness, genre preference, and technology used

This research examined how multiple factors (i.e., hours of gameplay, types of gameplay, preferred genre of video games, technology used to play games, and biological sex) were associated with both trait and situational verbal aggressiveness. Cross-sectional data were collected from 435 undergraduate students via an online questionnaire. Results indicated similar patterns to previous literature in that video gameplay hours were positively related to verbal aggressiveness. However, we extended research by also showing that a preference for certain genres and technology used to play video games were also related with both situational and trait verbal aggressiveness. Based on these results, we argue that player choice of genre and technology should be considered when examining the relationship between video games and verbal aggressiveness.

СТИМУЛЯЦІЯ ПІЗНАВАЛЬНИХ ПРОЦЕСІВ ПСИХІКИ ЗАСОБАМИ ОНЛАЙН-ІГОР

The article presents the results of testing of the developed online-training of cognitive functions of the brain, which was implemented on a separately created game platform using a set of specially developed games. The proposed results reveal the theoretical justification of the cognitive functions, which are selected for training: memory, thinking, attention, imagination, perception. The characteristics of each cognitive function to be stimulated in the conditions of online games are highlighted. We described the algorithm for games evaluating according to certain cognitive functions and their characteristics. An analysis of the effectiveness of evaluation is given. The results of completing by players of game sessions balanced on the chosen cognitive functions are described. A comparison of the effectiveness of stimulation of different cognitive functions depending on the motivation and choice of players is presented. The aim of the study was to evaluate the effectiveness of online games in activation and stimulation of the cognitive functions of the human psyche. The research methods were based on our own developed algorithm for training success estimation, analysis of the results of online games, methods of mathematical statistics. The results of the study are presented on the basis of approbation of the developed training, conducted during two years. The analysis of the implementation of the training revealed the dependence of the effectiveness of stimulation of cognitive functions on the motivation and choice of the player. It is shown that when training on a balanced set of games, where all cognitive functions are present in equal shares, the effectiveness of training is not uniform and not balanced. It is investigated that the effectiveness of training depends on the quality of the game's effect on cognitive function. In order to take into account this quality of influence, all games were divided into two classes: actively shaping and passively stimulating. The distribution of games by classes was experimentally confirmed. A generalized analysis of the game sessions of all players for six months revealed cognitive functions which for the most players were better developed and more stimulated.

Learning enables adaptation in cooperation for multi-player stochastic games.

Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g. the 'tragedy of the commons'), modulate their behaviours to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Using evolutionary game theory, we develop a framework of stochastic games that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviours can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favoured over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social-ecological dilemmas, learning enables the adaptation of individuals to varying environments.

The expected prisoner’s dilemma – With rationally arising cooperation

Currently, there is no satisfying answer to how cooperation arises rationally in a single-play prisoner’s dilemma game with complete information. When player types are known, as well as payoffs and actions, economic analysis through payoff-optimizing computation does not provide a clear path for cooperation. We propose a new form of game–the ‘expected’ game–and illustrate its implications for theory and practice based on the prisoner’s dilemma example. We prove that cooperation can be a rational choice for players in reality in such games defined by a weighted set of payoffs of two or more different reference games.

Popular Music in the Nostalgia Video Game: The Way It Never Sounded, by Andra Ivănescu

<i>Popular Music in the Nostalgia Video Game: The Way It Never Sounded</i>, by Andra Ivănescu

Discrete regimes of information reception in non-antagonistic repeated game

The gain functions depend on the choices of players and time. The set of choices of the second player is changed in time according to one of some variants. The true variant is ascertained during the game. The current information about the set of choices and about partner's choices is received as sample data. An optimal discrete procedure of obtaining information is found that allows preserving the equilibrium.

Optimization of information exchange in networks under conditions of uncertainty

The article considers the problem of optimizing the exchange of information in the conditions of uncertainty that arises in the process of functioning of converged networks. The uncertainties formulated in the paper are characterized by discrepancies in the "interests" of different network nodes, varying degrees of awareness or incomplete data on the value of system parameters. Such uncertainties lead to a decentralized process management strategy in a converged network. The problem of interaction of network nodes is considered from the point of view of game theory, at the same time players act as separate nodes of system, the exchange of information between which is limited by conditions of uncertainty. Decision-making procedures are formulated for the following cases: when one node is the center and the other opposes it and the center knows information about the choice of the second player; when the center does not know the capabilities of the second player, and the uncertainty lies in the random nature of the strategy of the second player; network node synergy model. For the first case, the optimal control is based on the maximum action of the first station (center) and the maximum resistance of the other station (second player) and can use the maximum model. For the second case, the limitations of the strategy of the game (including the strategy of punishment of the player) are determined and it is proved that the gain, more than the maximum guaranteed result of the center, cannot be obtained on any other class of strategies. The relation of the center to the uncertainty in the model based on the synergetic concept of the operation of convergent networks is such that it seeks to maximize the linear convolution of the values of its criterion. The peculiarities of constructing a strategy for the exchange of information between individual nodes of the network in the presence and absence of isolated points in the set of choices of the player who opposes the center are studied. The developed model of interaction of heterogeneous subsystems of the telecommunication network in the conditions of uncertainty can be used for situations when nodes have incomplete information about both the system as whole and other nodes.

ФОРМУВАННЯ СТРАТЕГІЇ УПРАВЛІННЯ РЕЖИМАМИ РОБОТИ СИСТЕМ ЗАХИСТУ НА ОСНОВІ МОДЕЛІ ІГРОВОГО УПРАВЛІННЯ

The main areas of application of game theory are economics, political science, tactical and military-strategic tasks, evolutionary biology and, more recently, information technology, security and artificial intelligence. Game theory studies the problems of decision-making of several people (players). It concerns the behavior of players whose decisions affect each other. The application of game theory in the field of modeling decision-making processes has different approaches, which are not systematized in the future, and sometimes contradict each other. Game theory is designed to solve situations in which the outcome of players' decisions depends not only on how they choose them, but also on the choices of other players with whom they interact. If we consider the field of information security, the peculiarity of the information conflict between the operational management system of information protection and the infringer who tries to gain unauthorized access is that opposing parties who have several ways of action can apply them repeatedly, choosing the best way based on information about the opposite parties. In this case, each step of resolving the conflict is characterized not by the final state, but by some payment function. In many situations, when designing information security systems, there is a need to develop and make decisions in conditions of uncertainty. Uncertainty can be of different nature. The planned actions of hackers, which are aimed at reducing the effectiveness of security systems, are uncertain. Uncertainty may relate to a risk situation in which the management system of the information network that decides on the application of the protection system is able to establish not only all possible results of decisions, but also the probability of possible conditions for their occurrence. Design conditions affect decision-making subconsciously, regardless of the actions of the decision-maker. When all the consequences of possible decisions are known, but their probability is unknown, it is obvious that decisions are made in conditions of complete uncertainty. The main promising theory of analysis of decision-making processes at the stage of designing information security systems is game theory. Therefore, there is a need to develop methods of operational (adaptive) management of information protection, depending on the availability of a priori information about the possibility of attacks by the infringer and his strategy to create unauthorized access to information resources. Game theory allows us to offer recommendations for the formation of management strategies for protection systems.

Modeling and Estimating the Impact of the OPEC Agreement on Oil Production in Russia

The shale revolution in the United States dramatically changed the situation on the global oil market. The response to the shale boom of 2013-2014 was the OPEC + agreement, which aims to regulate the market in order to stabilize oil prices. The article analyzes the impact of the OPEC+ agreement on the global oil market, taking into account the dynamics of US oil shale production. A simulation dynamic game model is proposed, which describes the relationship between the oil demand, the strategy of oil supplying by the players and the dynamics of oil price. The model allows, by setting different strategies for the behavior of players, to simulate the movement of oil prices, which in turn affect the choice of players in their decisions on oil production and its supply to the market. The model includes three players: US shale companies, OPEC+ countries (including Russia), and non-OPEC countries (including conventional US oil production). The research methodology is based on scenario modeling and analysis of OPEC+ strategies for managing the supply and demand balance (decrease or increase in production) for targeting oil prices at specified target levels. We tried in this study to answer the question: is the OPEC agreement favorable for Russia? Calculations show that for Russia, in terms of maintaining market share, the option of targeting oil prices at lower levels is the most profitable, but at the same time, gross oil revenues of Russia are noticeably reduced.

Descriptive complexity and revealed preference theory

This paper formalizes revealed preference theory using the notion of Ramsey eliminability in logic, and shows how the language required to state a revealed preference axiom for some choice theory is closely connected with the computational tractability of testing that theory. The connection is made through results in descriptive complexity theory, a relatively new field in finite model theory. It is shown that checking whether observed choices of players in normal form games are Nash rationalizable is an NP-complete problem. This also means that there does not exist an analogue (in a precise sense) of the Strong Axiom of Revealed Preference for Nash equilibrium.