Iterative Game Research Articles

Stackelberg-Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile-Edge Computing System

We study the intelligent offloading problem for a multiple unmanned aerial vehicle (multi-UAV)-assisted mobile edge computing (MEC) system in a MEC scenario where a natural disaster has damaged the edge server. The study has two steps. First, the task offloading destination is determined by minimizing the total energy consumption of the multiple UAVs in the system. We propose the server selection game-theoretic (SSGT) algorithm and demonstrate its convergence through simulation experiments. Second, we propose an offloading incentive mechanism to price computing resources for a single UAV-MEC server. Considering the UAV’s power consumption and mobile users’ willingness, we model the interaction between the UAV-MEC server and mobile users as a Stackelberg game. We prove the existence of a Nash equilibrium by theoretical analysis and experimental verification and design the multi-round iterative game (MRIG) algorithm based on arithmetic descent to achieve the optimal solution, i.e., the utility tradeoff between the UAV-MEC server and mobile users. Finally, the simulation results show that our proposed scheme can increase the value of overall user satisfaction (SoU) more than other schemes, which proves that the incentive mechanism of resource pricing can supply computing power support for ground mobile users in a UAV-assisted MEC system more effectively.

Study on the Calculation Method of Green GDP to Measure the Economic Health

This paper discusses the replacement of conventional GDP with green GDP (GGDP) as the main measure of economic health. Based on data on annual average temperature, annual precipitation, annual sunshine hours and annual average relative humidity for country C over the past decade, a multiple regression model is used to demonstrate that the linear effect of GGDP is positive for green growth. Further considering the differences in economic power between developed, developing and lagging countries, an infinite iterative game model of green GDP and conventional GDP is constructed based on the mutual constraints of long-term and short-term interests. Finally, considering the resistance to replacing GDP with green GDP, and in order to fully reflect the tortuous and complex relationship between climate change and social costs, an integrated model that will be embedded in the economic system, the DICE-2013R model, is constructed in order to achieve a shift from a single energy model to an integrated climate model. Normalising the data based on the model calcglobal scale.

Effect of Audio-Visual Appeal on Game Enjoyment: Sample from Turkey

A player’s subjective interaction with a digital game is referred to as player experience. The consequence of playing a game affects a player’s thoughts, feelings, attitudes, and behaviours. To measure player experience there are various qualitative and quantitative methods. Iterative game development and play testing sessions enhance and optimize game designs, to determine the impact of functional and psychosocial consequences of gaming in various cultures, a credible scale is required. To be able to measure and analyse player experience, this study aimed at adapting the ‘Player Experience Inventory’ (PXI) scale developed by V. V. Abeele1 to Turkish. The results of test-retest analysis and back-and-forth translation demonstrate that linguistic equivalence is not applicable for the Turkish variant. Only one item for functional – audio-visual appeal – and two items from psychosocial – immersion and autonomy – consequence of gaming have a proper factor structure. In this way an adaption study was carried out by confirmatory factor analysis (CFA) and explanatory factor analysis (EFA) with three items from the scale. The validity and reliability of the scale and relationship of audio-visual appeal of gaming on game enjoyment were tested and this article proposes a model for the functional and psychosocial consequences of gaming.

Distributed Logistics Resources Allocation with Blockchain, Smart Contract, and Edge Computing

The traditional centralized logistics resources allocation method can no longer adapt to the new business model of decentralized e-commerce, requiring transaction security for all parties involved in the logistics process. Utilizing blockchain and smart contract technologies to build logistics resources allocation network foundation and edge computing technology to assist the resource-constrained transport nodes in implementing complex computation, this paper proposes a distributed logistics resources allocation chain (DLRAChain) concept and designs a DLRAChain network that supports independent decision-making, fair bidding, and secure allocation of interests for all resources allocation participants. The corresponding system models are constructed according to the different roles of DLRAChain participants. Furthermore, the logistics resources requester–provider negotiation process is formulated as a two-stage Stackelberg game. To resolve the optimization problem of the game, the iterative game algorithm (IGA) and distributed logistics resources allocation algorithm (DLRAA) are proposed. Finally, the utility of warehouse and transport nodes and reward of mobile edge computing (MEC) nodes are analyzed with experimental simulation results. The results demonstrate that the proposed models adequately address the DLRA problem, and that the proposed game and corresponding algorithms efficiently achieve the optimal strategy, saving the response time of resources allocation participants.

Willingness to pay for social health insurance and its associated factors among public servants in Addis Ababa, Ethiopia: a cross-sectional study

BackgroundAs a means of establishing a sustained and fair health care financing system, Ethiopia has planned and ratified a legal framework to introduce a social health insurance program for employees of the formal sector to protect them against financial and health burdens. However, the implementation has been delayed due to the resistance of public servants to pay the proposed premium. The aim of this study was to estimate the magnitude of willingness to pay the proposed amount of premium set by the government for the social health insurance program and the factors associated with it among public servants in Addis Ababa, Ethiopia.MethodsAn institution-based cross-sectional study design was used to conduct the study. Multistage sampling was employed to select a total of 503 from 12 randomly selected public sectors. Data were collected using pretested, interviewer-administered structured questionnaires. A contingent valuation method with an iterative bidding game was used to elicit willingness to pay. Finally, logistic regression analysis was used to identify independent predictors of willingness to pay. Statistical significance was considered at P < 0.05 with adjusted odds ratios calculated at 95% CIs.ResultsOnly 35.4% were willing to pay the proposed premium (3% of their monthly salary). Those who had children from 6–18 years old (AOR = 3.252; 95% CI: 1.15, 9.22), had a history of unaffordable health service costs during the last 12 months (AOR = 9.631; 95% CI: 4.12, 22.52), and had prior information about the social health insurance program (AOR = 11.011, 95% CI. 3.735–32.462) were more likely to pay for the proposed social health insurance program compared to their counterparts.ConclusionThe willingness to pay the proposed amount premium for social health insurance among public servants in Addis Ababa was very low that implies the implementation will be challenging. Thus, the government of Ethiopia should consider reviewing the amount of premium contributions expected from employees before implementing the social health insurance scheme.

World-class interpretable poker

We address the problem of interpretability in iterative game solving for imperfect-information games such as poker. This lack of interpretability has two main sources: first, the use of an uninterpretable feature representation, and second, the use of black box methods such as neural networks, for the fitting procedure. In this paper, we present advances on both fronts. Namely, first we propose a novel, compact, and easy-to-understand game-state feature representation for Heads-up No-limit (HUNL) Poker. Second, we make use of globally optimal decision trees, paired with a counterfactual regret minimization (CFR) self-play algorithm, to train our poker bot which produces an entirely interpretable agent. Through experiments against Slumbot, the winner of the most recent Annual Computer Poker Competition, we demonstrate that our approach yields a HUNL Poker agent that is capable of beating the Slumbot. Most exciting of all, the resulting poker bot is highly interpretable, allowing humans to learn from the novel strategies it discovers.

Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: A real case study

This paper proposes a two-step framework for techno-economic analysis of a demand-side flexibility service in distribution networks. Step one applies optimization-based modelling to propose a generic problem formulation which determines the offer curve, in terms of available flexible capacity and its marginal cost, for flexible distribution-connected assets. These offer curves form an input to the second step, which uses a multi-agent iterative game framework to determine the benefits of demand-side flexibility for the Distribution System Operator (DSO) and the service providers. The combined two-step framework simultaneously accounts for the objectives of each flexibility provider, technical constraints of flexible assets, customer preferences, market clearing mechanisms, and strategic bidding by service providers, omission of any of which can lead to erroneous results. The proposed two-step framework has been applied to a real case study in the North East of England to examine four market mechanisms and three bidding strategies. The results showed that among all considered market mechanisms, flexibility markets that operate under discriminatory pricing, such as pay-as-bid and Dutch reverse auctions, are prone to manipulations, especially in the lack of competition. In contrast, uniform pricing pay-as-cleared auction provides limited opportunities for manipulation even when competition is low.

New champions of preferential trade? Two-level games in China’s and India’s shifting commercial strategies

Following decades of relative isolation, China and India have become the world’s largest new traders. In this paper, we focus on their Preferential Trade Agreements (PTAs). While the two economies initially followed similar paths, with a growing number of PTAs signed in the first decade of the 21st Century, since 2011 India has taken a U-turn and stopped completing them. China, on the other hand, has widened and deepened its trade agreements. We present a novel theoretical framework to analyze international economic negotiations by emerging economies and use it to study the puzzling divergence of the trade policies of China and India. By adapting the two-level game framework to emerging economies, we argue that there are key differences in the political economies of countries like China and India (compared to Western industrialized ones), which requires a more specific focus on the domestic side of the two-level game. We show that accounting for non-legislative domestic ratification processes and for iterative games and experiential learning by domestic actors are crucial in understanding the trade strategies of emerging economies. While much of the literature explains large emerging economies by looking at external systemic factors, we instead suggest that their domestic politics trumps international politics.

Abnormal Responses in Cognitive Impulsivity Circuits Are Associated with Glycosylated Hemoglobin Trajectories in Type 1 Diabetes Mellitus and Impaired Metabolic Control.

Risky health decisions and impulse control profiles may impact on metabolic control in type 1 diabetes mellitus (T1DM). We hypothesize that the neural correlates of cognitive impulsivity and decision-making in T1DM relate to metabolic control trajectories. We combined functional magnetic resonance imaging (fMRI), measures of metabolic trajectories (glycosylated hemoglobin [HbA1c] over multiple time points) and behavioral assessment using a cognitive impulsivity paradigm, the Balloon Analogue Risk Task (BART), in 50 participants (25 T1DM and 25 controls). Behavioral results showed that T1DM participants followed a rigid conservative risk strategy along the iterative game. Imaging group comparisons showed that patients showed larger activation of reward related, limbic regions (nucleus accumbens, amygdala) and insula (interoceptive saliency network) in initial game stages. Upon game completion differences emerged in relation to error monitoring (anterior cingulate cortex [ACC]) and inhibitory control (inferior frontal gyrus). Importantly, activity in the saliency network (ACC and insula), which monitors interoceptive states, was related with metabolic trajectories, which was also found for limbic/reward networks. Parietal and posterior cingulate regions activated both in controls and patients with adaptive decision-making, and positively associated with metabolic trajectories. We found triple converging evidence when comparing metabolic trajectories, patients versus controls or risk averse (non-learners) versus patients who learned by trial and error. Dopaminergic reward and saliency (interoceptive and error monitoring) circuits show a tight link with impaired metabolic trajectories and cognitive impulsivity in T1DM. Activity in parietal and posterior cingulate are associated with adaptive trajectories. This link between reward-saliency-inhibition circuits suggests novel strategies for patient management.

Quantum algorithm for online convex optimization

We explore whether quantum advantages can be found for the zeroth-order online convex optimization (OCO) problem, which is also known as bandit convex optimization with multi-point feedback. In this setting, given access to zeroth-order oracles (that is, the loss function is accessed as a black box that returns the function value for any queried input), a player attempts to minimize a sequence of adversarially generated convex loss functions. This procedure can be described as a T round iterative game between the player and the adversary. In this paper, we present quantum algorithms for the problem and show for the first time that potential quantum advantages are possible for problems of OCO. Specifically, our contributions are as follows. (i) When the player is allowed to query zeroth-order oracles O(1) times in each round as feedback, we give a quantum algorithm that achieves regret without additional dependence of the dimension n, which outperforms the already known optimal classical algorithm only achieving regret. Note that the regret of our quantum algorithm has achieved the lower bound of classical first-order methods. (ii) We show that for strongly convex loss functions, the quantum algorithm can achieve O(log T) regret with O(1) queries as well, which means that the quantum algorithm can achieve the same regret bound as the classical algorithms in the full information setting.

Supply chain ethics and transparency: An agent‐based model approach with Q‐learning agents

This study examines suppliers' decision making regarding supply chain ethics and transparency using an agent‐based model with Q‐learning agents. A supplier may not accurately identify the demand for its product because the impact of supply chain ethics on the market is opaque. Thus, we define each supplier as a Q‐learning agent that learns via an iterative game process. Our simulation results show that suppliers maintain low levels of ethics and transparency, which is a collusion behavior. Several suppliers deviate from collusion, but most of the suppliers collude to manage the supply chain unethically and opaquely.

A Smartphone Serious Game for Adolescents (Grow It! App): Development, Feasibility, and Acceptance Study.

BackgroundAnxiety and mood problems in adolescents often go unnoticed and may therefore remain untreated. Identifying and preventing the development of emotional problems requires monitoring and effective tools to strengthen adolescents' resilience, for example, by enhancing coping skills.ObjectiveThis study describes the developmental process, feasibility, and acceptance of Grow It!, a multiplayer serious game app for adolescents aged 12-25 years. The app consists of the experience sampling method (ESM) to monitor thoughts, behaviors, and emotions in daily life to enhance self-insight and daily cognitive behavioral therapy–based challenges to promote adaptive coping.MethodsOur approach entails an iterative game design process combined with an agile method to develop the smartphone app. The incorporated game features (ie, challenges, chat functionality, and visual representation) in the Grow It! app were co-designed with adolescent end users to increase participant engagement and adherence.ResultsThe Grow It! app was delivered for Android and iOS in May 2020. Grow It! was offered to adolescents during the COVID-19 crisis between May and December 2020. Participants of the Grow It! COVID-19 study (sample 1: N=685; mean age 16.19, SD 3.11 years; 193/685, 28.2% boys; sample 2: N=1035; mean age 18.78, SD 3.51 years; 193/1035, 18.64% boys) completed 31.5% (13.2/42) to 49.5% (10.4/21) of challenges. Compliance of ESM was suboptimal (35.1/210, 16.7% to 32.5/105, 30.9%). Follow-up questionnaires indicated an overall score of the app of 7.1 out of 10. Moreover, 72.6% (278/383) to 75.6% (487/644) would recommend the app to friends.ConclusionsTo our knowledge, Grow It! is the first gamified ESM app that both measures individual differences in emotional dynamics and offers an integrated cognitive behavioral therapy–based intervention. Our findings support the feasibility and acceptance, and therefore applicability, of the Grow It! app in adolescents. Further iterations of this serious game app will focus on the increase of compliance and on providing participants feedback through their personal mood profiles.

Initial Design and Testing of Multiplayer Cooperative Game to Support Physical Activity in Schools

Recent studies have shown that children are not adequately physically active and there is a need to increase children’s physical activity. This study describes new opportunities and solutions for using existing games and gamification to increase physical activity among children in Sweden. We adopted the principles of Tic-Tac-Training to redesign, build, and test a classical multiplayer cooperative game, Battleship, to create a PA game that children experience as fun and engaging. The low fidelity prototype of the game was developed using an iterative game development life cycle and tested with 13 young male children aged 8–11 in a real-world informal setting. A mixed-method research approach was used to understand the users’ experiences and the impact of the Battleship-PA game on behavior change regarding physical activity. Research data were collected through audio recordings of interactions, direct observation, and a user experience questionnaire. The results of this study indicate both positive and negative feedback that can be used to improve the game and user experiences. The results from the unfiltered recordings revealed that both teams were competitive, cooperated within their team, and became excited whenever they destroyed opponent’s ships or were close to winning. However, the children felt bored and exhausted when many gamification tasks were repeated several times in a game session. Direct observation indicated that the children enjoyed the physical activities resulting from playing the game. However, participants who had not previously played the classical version of Battleship were confused about the objectives and concept of the game. The analysis of the user experience questionnaire indicated that most children found the game easy to play, motivating, engaging, interactive, fun, cooperative, competitive, and visually appealing. Furthermore, most children agreed that the game helped them to be physically active and strongly agreed that they enjoyed performing the physical activities in the game. Future work is needed to improve the game user interface, gamification elements, and prepare additional physical activity tasks for a rewarding experience.

Correlation Filters Based on Multi-Expert and Game Theory for Visual Object Tracking

Most existing correlation filters based tracking algorithms only use the linear additive features or simple fusion features, which are unable to fully learn the appearance information of the target. In this paper, a multi-expert and game theory correlation filter (MEGTCF) algorithm is proposed to treat the aforementioned problems. The algorithm extracts a variety of handcrafted features and deep features of the target and forms multiple experts in different combinations to track the target in parallel. In addition, the game theory strategy is introduced to obtain more reliable decisions. The expert evaluation mechanism evaluates and selects the most discriminative experts as game participants and fully explores the complementary information among different expert features through the iterative game, so as to realize the information interaction between expert features. We also propose a confidence indicator to calculate the weights of the experts with higher scores and adaptively update the expert model to avoid expert corruption. Extensive experiments are conducted on five popular benchmarks, including OTB2013, OTB2015, VOT2018, LaSOT, and the UAV123 tracking benchmark. The success rate and precision of the MEGTCF tracker on the OTB2015 reach 67.8 % and 91.4 %, which outperforms other state-of-the-art trackers based on correlation filters. In addition, the MEGTCF tracker is also highly suitable for challenging scenarios such as object deformation, background clutter, and occlusion.

Two-Layer Game-Based Framework for Local Energy Flexibility Trading

A new configuration is required to model the behavior of customers, aggregators, the distribution system operator (DSO), and their interactions due to the active participation of customers in the local flexibility market. To this end, we propose a two-layer game-based framework that models agents&#x2019; behavior and their interactions. Thus, firstly, at the inner layer, customers and aggregators set their decision variables considering the decisions of each other performing an iterative game. After the inner layer game concludes, in the outer layer, the DSO determines its decision variable according to the decision of aggregators and customers. If the convergence condition is satisfied, the game of the outer layer concludes. Otherwise, there is another inner game and subsequent outer game until the satisfaction of convergence condition. Therefor, customers, aggregators, and the DSO have similar decision-making power. Since all of them can make their own decisions and modify them according to others&#x2019; decisions. To study our model, we consider three scenarios with different levels of freedom while decision-making for customers that is resulted from different levels of limitation for arbitrage avoidance. Our results illustrate that our iterative approach is converged after few iterations in both the inner and the outer layer. Moreover, customers who have a contract with the same aggregator behave similarly. Furthermore, aggregators benefit from customers&#x2019; freedom, while it is very destructive for the DSO and increases its objective function.

An iterative security game for computing robust and adaptive network flows

We study the robust and adaptive maximum network flow problem in an uncertain environment where the network parameters (e.g., capacities) are known and deterministic, but the network structure (e.g., edges) is vulnerable to adversarial attacks or failures. We propose a robust and sustainable network flow model to effectively and proactively counter plausible attacking behaviors of an adversary operating under a budget constraint. Specifically, we introduce a novel scenario generation approach based on an iterative two-player game between a defender and an adversary. We assume that the adversary always takes a best myopic response (out of some feasible attacks) against the current flow scenario prepared by the defender. On the other hand, we assume that the defender considers all the attacking behaviors revealed by the adversary in previous iterations of the game in order to generate a new conservative flow strategy that is robust (maximin) against all those attacks. This iterative game continues until the objectives of the adversary and the administrator both converge. We show that the robust network flow problem to be solved by the defender is NP-hard and that the complexity of the adversary’s decision problem grows exponentially with the network size and the adversary’s budget value. We propose two principled heuristic approaches for solving the adversary’s problem at the scale of a large urban network. Extensive computational results on multiple synthetic and real-world data sets demonstrate that the solution provided by the defender’s problem significantly increases the amount of flow pushed through the network and reduces the expected amount of lost flow over four state-of-the-art benchmark approaches.

Predicting mood behaviors of prisoner’s dilemma players

Over the last few years, attention goes to the model of Three-Player Prisoner’s Dilemma Iterated Game. In this work we are concerned with studying the competition between strategies with special behavior in this model. These strategies have behavior similar to Tit for Tat strategy. We focus on strategies with angry behavior and strategies with tolerant behavior. The results reveal the qualities of the winning strategy. From our studies we can judge the social behavior that must be enjoyed by the person who follows the winning strategy. A learning algorithm has been designed and a multi-agent system has been developed to obtain our results.

Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading

Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading

Social Associative Learning and Trust Formation Across Adulthood

Trust is a key component of social interaction. Older adults, however, often exhibit excessive trust relative to younger adults. One explanation is that older adults may learn to trust differently than younger adults. Here, we report a study examining how younger (N=36) and older adults (N=37) learn to trust over time. Participants completed a classic iterative trust game with three partners (15 trials each). Younger and older adults shared similar amounts but there were differences in how they shared that money. Compared to younger adults, older adults invested more with untrustworthy partners and less with trustworthy partners. As a group, older adults displayed less learning than younger adults and computational modeling suggests that older adults used different learning strategies. These findings suggest that older adults attend to and learn from social cues differently from younger adults. Neuroimaging results focused on reward processing will also be discussed.

Spaces of Joint Inquiry Through Visual Facilitation and Representations in Higher Education: An Exploratory case study

This study investigates how the use of visual facilitation and representations, e.g. visualisations and video productions, combined with peer‑feedback sessions can create exploratory approaches to game design in online teaching. The article analyses an iterative game development process in an online learning context. The empirical data is primarily based on an explorative case study of “Games for change”; a course held in 2018 in which master students from the international Nordic Visual Studies and Art Education (NoVA) design games that address issues in society. Throughout the course, the students from universities in Finland, Sweden and Denmark engaged in a cross‑cultural collaboration across campuses. The purpose of the study was to explore how to establish an online space for joint design inquiry in the context of ‘games for change’ across time and space as well as cultural and professional barriers. The data used for analysis includes teaching observations, videos of play sessions, photos and visual representations, students’ reflection papers and students’ written and oral evaluations after completion of the course. The analysis is based on different problem‑based learning (PBL) activities; lectures, video instructions, presentation‑ and feedback sessions, reflexive exercises and students’ self‑directed design and learning processes in groups. As part of the game course, teachers presented game theory and exercises through videos and visualisations to support the students’ iterative game design processes. The analysis of the PBL activities shows that teachers’ video instructions relating theoretical game concepts to the students’ actual group work supported the introduction to the game field as well as their design processes. The balance between the value of video instructions with specific feedback and teachers’ time for preparation is a relevant issue for further exploration in online teaching. Moreover, findings show that the students’ visualisations and video productions exemplifying game situations created a visible reference point for further discussions in feedback sessions across campuses, which guided game development. Thus, the combination of inquiry approaches, critical game theory and design processes combined with students’ visualisations and video productions provides interesting connections for bridging gaps between cultures and professions, e.g. in art and games. By the rich and visual descriptions of PBL activities, student work and reflective evaluations, the exploratory case study can function as inspiration for applying similar approaches to new local contexts in higher education.