Strategy Space Research Articles

Bayesian Fictitious Play in Oligopoly: The Case of Risk-Averse Agents

A number of learning models have been suggested to analyze the repeated interaction of boundedly rational agents competing in oligopolistic markets. The agents form a model of the environment that they are competing in, which includes the market demand and price formation process, as well as their expectations of their rivals’ actions. The agents update their model based on the observed output and price realizations and then choose their next period output levels according to an optimization criterion. In previous works, the global dynamics of price movement have been analyzed when risk-neutral agents maximize their expected rewards at each round. However, in many practical settings, agents may be concerned with the risk or uncertainty in their reward stream, in addition to the expected value of the future rewards. Learning in oligopoly models for the case of risk-averse agents has received much less attention. In this paper, we present a novel learning model that extends fictitious play learning to continuous strategy spaces where agents combine their prior beliefs with market price realizations in previous periods to learn the mean and the variance of the aggregate supply function of the rival firms in a Bayesian framework. Next, each firm maximizes a linear combination of the expected value of the profit and a penalty term for the variance of the returns. Specifically, each agent assumes that the aggregate supply of the remaining agents is sampled from a parametric distribution employing a normal-inverse gamma prior. We prove the convergence of the proposed dynamics and present simulation results to compare the proposed learning rule to the traditional best response dynamics.

Optimal private payoff manipulation against commitment in extensive-form games

Stackelberg equilibrium describes the optimal strategies of a player, when she (the leader) first credibly commits to a strategy. Her opponent (the follower) will best respond to her commitment. To compute the optimal commitment, a leader must learn enough follower's payoff information. The follower can then potentially provide fake information, to induce a different final game outcome that benefits him more than when he truthfully behaves.We study such follower's manipulation in extensive-form games. For all four settings considered, we characterize all the inducible game outcomes. We show the polynomial-time tractability of finding the optimal payoff function to misreport. We compare the follower's optimal attainable utilities among different settings, with the true game fixed. In particular, one comparison shows that the follower gets no less when the leader's strategy space expands from pure strategies to behavioral strategies. Our work completely resolves this follower's optimal manipulation problem on extensive-form game trees.

Care workers, collectivisation and protest: a case for feminist and anti-capitalist resistance against the state

Nation-wide organised protests by Accredited Social Health Activists (ASHAs), arguably the backbone of India's fragmented healthcare system, form a watershed moment in feminist protest in India by centring demands for the regularisation of their ‘voluntary’ and undercompensated work. This distinct subaltern group (the ASHAs) engages with the state through dynamic political protest. This article critically explores the protest strategies adopted by ASHAs to better understand feminist protest on the Indian political landscape. It adopts a reflexive method of inquiry by unpacking how ASHA policy perpetuates institutional patterns of gender-based occupational segregation and how ASHAs, in turn, navigate and respond to these challenges. Somewhere between victimhood and agency, ASHA workers have reclaimed a site of struggle and resistance. Indeed, in the face of gender-based discrimination, ASHA workers are agitating on their own terms. A postcolonial feminist lens allows us to explore the axes of resistance adopted by ASHAs, including their medium of protest, which offers a space for different performative strategies: the self-sustained and self-contained leadership and mobilisation in which ASHAs reject gender-based stereotypes, exclusion, disadvantage and discrimination; and the use of protest to build sites of resistance, solidarity and negotiation. This theoretical framework offers valuable insight by shifting focus away from judicial strategies and towards ASHA workers as ‘protesters’ and as authors of their own narrative.

Optimization of laser powder bed fusion process parameter for the fabrication of AlSi12 using NSGA‐II and Pareto search algorithm

AbstractAdditive manufacturing, notably laser powder bed fusion (LPBF), excels in producing complex geometries and is widely used in the automotive, aerospace, and naval industries. Laser powder bed fusion enables the creation of components with the required stiffness and strength at a lighter weight than traditional manufacturing methods. Aluminium alloys are particularly promising for laser powder bed fusion in the automotive and aerospace sectors. To enhance the effectiveness of laser powder bed fusion‐produced components, optimized process parameters must be designed for specific materials. This study investigates the influence of processing parameters, scan speed, scan strategy, and hatch space, on the relative density, surface roughness, and microhardness of AlSi12 samples fabricated by laser powder bed fusion. A Taguchi L27 orthogonal array was used to systematically analyze the effects of these parameters. A regression model was developed and evaluated through analysis of variance using signal‐to‐noise (S/N) ratios to identify optimal parameter values. Results indicated that the scan pattern significantly affects relative density, while hatch space impacts surface roughness and microhardness. Optimal solutions were obtained through multi‐objective optimization using the non‐dominated sorting genetic algorithm (NSGA‐II) and Pareto search algorithms. Experimental validation showed average errors of 0.483 % and 0.461 % for NSGA‐II and Pareto search algorithms, respectively.

Monte-Carlo Regret Minimization for Adversarial Team Games

We study equilibrium approximation in extensive-form adversarial team games, in which two teams of rational players compete in a zero-sum interaction. The suitable solution concept in these settings is the Team-Maxmin Equilibrium with Correlation (TMECor), which naturally arises when the team players play ex-ante correlated strategies. While computing such an equilibrium is APX-hard, recent techniques show that scalability beyond toy instances is possible. However, even compact representations of the team’s strategy space, such as that exploiting Directed Acyclic Graphs (DAGs), have exponential size prohibiting solving large instances. In the present paper, we show that Monte Carlo sampling for regret minimization in adversarial team games can provide an important advancement. In particular, we design a DAG Monte Carlo Counterfactual Regret Minimization algorithm that performs outcome sampling with O ( d ) time complexity per iteration, where d is the depth of the DAG, and with a convergence rate bound of O ( b kd ) , where b is the branching factor and k is the maximum number of private states in each public state of the team. We empirically evaluate our algorithms with a standard testbed of games, showing their performance when approximating equilibria. We investigate both cases in which only one team is composed of multiple players and cases in which both teams are composed of multiple players. Our empirical results show that our algorithms can provide a rough approximation to equilibrium strategies in all game instances considered, trading off a lower precision in the computed equilibrium for lower resource utilization.

The conditionality of the sovereignty of the psychological space of the individual by coping strategies and mechanisms of psychological protection for people from single-parent and large families

The article presents the results of an empirical study of the relationship between the sovereignty of the psychological space of personality, coping strategies and mechanisms of psychological protection of personality. Respondents who grew up in families with different numbers of children demonstrate the specific features of their coping behaviors and mechanisms of psychological protection in relation to indicators of the sovereignty of the psychological space of the individual.

NIGA: A Novel Method for Investigating the Attacker–Defender Model within Critical Infrastructure Networks

The field of infrastructure security has garnered significant research attention. By integrating complex network theory with game theory, researchers have proposed many methods for studying the interactions between the attacker and the defender from a macroscopic viewpoint. We constructed a game model of infrastructure networks to analyze attacker-defender confrontations. To address the challenge of finding the Nash equilibrium, we developed a novel algorithm—node-incremental greedy algorithm (NIGA)—which uses less strategy space to solve the problem. The experiments performed further showed that NIGA has better optimization ability than other traditional algorithms. The optimal defense strategies under different conditions of initial strategy ratios and attacker-defender resources were analyzed in this study. Using intelligent computing to solve the Nash equilibrium is a new approach by which for researchers to analyze attacker-defender confrontations.

Multi-strategy adaptive data augmentation for Graph Neural Networks

While existing Graph Neural Networks (GNNs) have demonstrated exceptional performance in semi-supervised learning, they are criticized for not fully utilizing unlabeled data and graph structure, leading to over-fitting. Recently, data augmentation as a technique gaining significant attention recently for enhancing the generalization capabilities of Graph Neural Networks (GNNs), is regarded as a promising solution to mitigate the issue of over-fitting. Nonetheless, existing studies focus on designing a simple augmentation strategy tailored to a specific dataset, without ensuring a diverse and consistent distribution of augmented graphs during the training process. Moreover, formulating different graph augmentation strategies for diverse datasets relies heavily on domain knowledge due to the distinct distributions inherent in various graphs. Designing a universal augmentation strategy which can adapt to different datasets remains a challenging endeavor. To resolve the thorny issue, we introduce an adaptive augmentation framework called MSA-AUG, which can adaptively integrate multiple heuristic strategies for GNNs. Specifically, in order to enhance the representation quality of GNNs, we first take advantage of various augmentation strategies from global, local and label perspectives. Then we integrate them into a set of candidate strategies, which is model-agnostic for any GNNs. Then, we construct a search space of candidate strategies and take each candidate-strategy with different selection weights and operation magnitudes. The learned parameters are leveraged to explore different selection weights and operation magnitudes for ensuring that the contribution of various graph augmentation strategies is well-adjusted. Finally, we employ a search algorithm based on density matching to dynamically explore the candidate strategies and take the best augmentation strategy on various datasets with different distributions for improving the generalization ability of GNNs. Extensive experiments on diverse benchmark datasets demonstrate that MSA-AUG can improve the performance of GNNs with varying structures of backbones.

3D printing of reprogrammable liquid crystal elastomers with exchangeable boronic ester bonds

Liquid crystal elastomers (LCEs) are a kind of soft actuating materials with large reversible deformation ability, which can work as the “motor” to exhibit complex deformations and drive the locomotion of soft robots. The deformation of LCEs depends on the three-dimensional (3D) shape of whole structure and alignment patterns of mesogens. Various methods have been employed to fabricate the LCE structure with desired shapes and mesogen alignments. However, conventional 3D printed LCEs require continuous thermal energy input to maintain their actuated shapes. The LCEs cannot be reprocessed and reprogrammed once cured. Herein, we introduce dynamic boronic ester bonds into the ink, with which the printed LCE structures are capable of being reprogrammed from polydomain into monodomain state and vice versa. We further explore the effects of printing parameters and content of dynamic covalent bonds on the actuation performance and reprogramming ability. The actuated shape could be well predicted with finite element method. The dynamic printable LCEs developed here offer new strategy and large design space for LCE structures.

Surrogate-based integrated design optimization for aerodynamic/stealth performance enhancements

The shape optimization considering both aerodynamic and radar stealth performances has been a considerable attention area for next-generation aircraft, with challenges involving balancing design contradictions and improving optimization efficiency. This paper develops a surrogate-based integrated design optimization method coupling Computational Fluid Dynamics (CFD) and Computational Electromagnetics (CEM) for aerodynamic/stealth performance enhancements. The topology-based 3D hexahedral mesh generation and 2D triangular mesh generation with adaptive refinement provide efficient preprocessing for CFD/CEM solvers. The surrogate-based optimization algorithm with parallel infill-sampling strategy and adaptive design space expansion is employed to search the global optimum and improve optimization efficiency while filtering out numerical noise. The Weighted Sum method is employed to address design contradictions in different disciplines. Optimizations considering aerodynamic, stealth, and aerodynamic/stealth performance of a flying wing aircraft are conducted to verify the effectiveness of the developed method. The optimization results indicate that exclusive consideration of a singular discipline leads to a significant deterioration in the performance of the other discipline. Balanced performance is achieved through coupling optimization. The numerical noise in the stealth discipline is markedly higher than in the aerodynamics discipline. The results demonstrate that the developed method is capable of addressing aerodynamic/stealth design optimization problems and significantly reducing the number of CFD/CEM evaluations while maintaining global optimization effectiveness.

A novel hybrid method integrating multi-objective optimization with emergy analysis for building renewal strategy

Building renewal has become a promising approach for improving energy efficiency and reaching carbon neutrality targets. Two renewal strategies, namely, the retrofitting of existing buildings and their demolition and reconstruction of a new one, are often explored and compared to determine which strategy is the optimal one. However, the decision is still uncertain due to the large domain space of strategies and the lack of comprehensive methods. This paper, therefore, presents a novel hybrid method to help decision-makers identify the optimal building renewal strategy based on the integration of multi-objective optimization algorithm with emergy analysis. A simulation-based non-dominated sorting genetic algorithm is developed to select the optimal retrofitting strategy, where dynamic simulation model is created in EnergyPlus software and non-dominated sorting genetic algorithm is written in Python. Then, the equivalent reconstruction strategy is projected by the optimal retrofitting strategy while meeting mandatory standards. A comparison between the optimal retrofitting strategy and the equivalent reconstruction strategy can be made by applying emergy analysis considering the whole life cycle phase. A fictional office building was chosen as a case study to illustrate the practical implementation of the proposed method. High-cost building retrofitting strategies were found to consistently yield the highest energy savings and thermal comfort, and the energy use decreased by 4.63 % to 48.25 %. The input emergy of the original building, retrofitting building and reconstruction building are 3.16E + 20 sej, 1.41E + 21 sej, and 1.46E + 21 sej, respectively, and the output emergy are 2.39E + 20 sej, 1.66E + 20 sej, and 4.76E + 19 sej. The results also indicate that the sustainability of the retrofitting strategy is better than that of the reconstruction strategy. This paper provides a guidance method and framework for decision-makers and policy-makers to identify the optimal building renewal strategy for reducing the energy use and achieving carbon peak targets.

A vast space of compact strategies for effective decisions.

Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.

Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.

Reducing betrayal behavior in green building construction: A quantum game approach

The issue of energy consumption in buildings is becoming increasingly pertinent. Society highly values energy conservation and environmental protection, viewing the adoption of green buildings as an inevitable trajectory in the evolution of the construction sector. However, a challenge emerges during the progression of green building construction concerning betrayal within the supply chain. This paper employs a quantum game approach to tackle the issue of green building betrayal within a two-tier green building supply chain comprising developers and contractors. Initially, a non-zero-sum classical game model is formulated based on the green-building construction process between developers and contractors Subsequently, the classical strategy space is extended to the quantum strategy space. The findings indicate that in classical games, and without considering quantum entanglement, even if both parties adopt comprehensive strategies, the party exerting complete effort still faces the risk of betrayal. Conversely, in scenarios of maximum quantum entanglement, the loss incurred by the party not exerting complete effort does not affect the party making a complete effort. Consequently, when accounting for quantum entanglement, both parties tend to opt for complete effort. Lastly, an entanglement treaty is proposed to constrain the strategic choices of both parties and ensure that neither has an incentive to betray. The findings of this study provide good suggestions for green building project managers to improve the effort degree of the developer and general construction contractor in green buildings to promote the development of green buildings.

Strategic Management of Small and Medium-Sized Enterprises - Is There Space for Functional Strategies?

Based on strategic management principles, it is reasonable to assume that lower-level units within the organizational structure, such as business and functional levels, will be carrying out the strategies. However, such organizational units should be considered not only as decision implementers, but also as strategists and program developers for the areas of activity in which they operate. Managers at the functional level solve problems related to setting goals and objectives in areas such as marketing, resource allocation, finance, production, and accounting. There is a lack of research on functional strategies, emphasizing the need for additional studies and publications. The goal of this paper is to outline significant elements of functional strategies, with a particular emphasis on their distinctiveness in comparison to other strategic aspects of management in small and medium-sized enterprises (SMEs). Additionally, the paper aims to explore the feasibility of implementing such strategies within these enterprises. The study seeks to accomplish its objectives through the utilization of theoretical deduction, in conjunction with the analysis of data derived from a concise survey of SMEs and pertinent practical illustrations. From a scientific perspective, the study's findings are predominantly positive rather than normative-regulatory in nature.

Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies

In vertical inhibition treatment strategies, multiple components of an intracellular pathway are simultaneously inhibited. Vertical inhibition of the BRAFV600E–MEK-ERK signalling pathway is a standard of care for treating BRAFV600E-mutated melanoma where two targeted cancer drugs, a BRAFV600E-inhibitor, and a MEK inhibitor, are administered in combination. Targeted therapies have been linked to early onsets of drug resistance, and thus treatment strategies of higher complexities and lower doses have been proposed as alternatives to current clinical strategies. However, finding optimal complex, low-dose treatment strategies is a challenge, as it is possible to design more treatment strategies than are feasibly testable in experimental settings. To quantitatively address this challenge, we develop a mathematical model of BRAFV600E–MEK-ERK signalling dynamics in response to combinations of the BRAFV600E-inhibitor dabrafenib (DBF), the MEK inhibitor trametinib (TMT), and the ERK-inhibitor SCH772984 (SCH). From a model of the BRAFV600E–MEK–ERK pathway, and a set of molecular-level drug–protein interactions, we extract a system of chemical reactions that is parameterised by in vitro data and converted to a system of ordinary differential equations (ODEs) using the law of mass action. The ODEs are solved numerically to produce simulations of how pathway-component concentrations change over time in response to different treatment strategies, i.e., inhibitor combinations and doses. The model can thus be used to limit the search space for effective treatment strategies that target the BRAFV600E–MEK–ERK pathway and warrant further experimental investigation. The results demonstrate that DBF and DBF–TMT–SCH therapies show marked sensitivity to BRAFV600E concentrations in silico, whilst TMT and SCH monotherapies do not.

A dynamic model of resource extraction and intensity of competition

AbstractThis paper represents a significant extension of the traditional Cournot extraction game, by introducing the linear supply function strategy space in a novel, dynamic way. A variety of asymmetric cases are considered, including the new firm‐specific competitive strategies. The results highlight the importance of competition in accelerating the supply of critical minerals, which is vital to mitigate climate change, but also present a new sustainability challenge.

A method of network attack-defense game and collaborative defense decision-making based on hierarchical multi-agent reinforcement learning

Faced with the challenges of security strategy design brought about by the complexity of attack behavior and the dynamism of network structure, dynamic hierarchical intelligent defense methods have shown their effectiveness. However, in complex network environments, their application requires a higher level of coordination mechanisms. Therefore, this paper proposes a hierarchical multi-agent reinforcement learning network attack and defense game and cooperative defense decision-making method, which autonomously and efficiently completes the formulation of defense strategies and defense behavior responses. Firstly, we construct a Stackelberg hypergame model of cyberspace conflicts, and under the condition of information loss, characterize the multi-layer dynamic defense coordination response mechanism. Secondly, By utilizing a hierarchical multi-agent reinforcement learning method as the driving force for game evolution, we sequentially solve the Nash equilibrium of the game, and form a dynamic autonomous defense strategy. Finally, we construct a hierarchical multi-agent reinforcement learning framework, which decouples the defense decision problem, reduces the dimension of the defense action space and the exploration difficulty of the strategy space, and learns coordinated defense strategies more efficiently. We used the CybORG (Cyber Operations Research Gym) environment for simulation. We compared and analyzed the autonomously generated cyber defense strategies with other related works, verifying the superior coordination performance of our method in defense strategy generation and control.

Repeated prisoner's dilemma games in multi-player structured populations with crosstalk

Crosstalk represents a ubiquitous phenomenon in which strategic moves made in one game can influence actions in another. Reiter et al. [1] first integrated crosstalk with the repeated prisoner's dilemma model in structured populations. We extend their strategic space from one-step reactive strategies that consider only the actions of the opponent to memory-one strategies that consider the actions of both parties. We explore the cooperation rate of some representative memory-one strategies in repeated prisoner's dilemma games for multi-player structured populations when crosstalk exists, and focus on finding evolutionarily stable strategies that are cooperative in the memory-one strategy space under the impact of crosstalk. Simulation results in the strategic dynamics show that appropriately forgiving defection is conducive to cooperative stability in the population when crosstalk exists. Specifically, for strategies that can promote cooperation, there is a mutual restriction between tolerating the behavior of both defecting (the focused player and his opponent both defecting, DD) and forgiving unilateral defecting (the focused player cooperating while his opponent defecting, CD). That is, a more forgiving behavior of CD requires a lower tolerance for DD, while a higher tolerance of DD requires a less forgiving behavior of CD. Notably, we find two effective strategies, namely, Win-Stay-Lose-Shift (WSLS) and Firm‑but-Fair (FBF) in the facilitation of cooperation in game dynamics with crosstalk. Moreover, we reveal that the structured population can better suppress the negative impact of crosstalk on cooperation. This investigation provides complementary insights into the interplay between strategic decision-making and the influence of crosstalk within the context of memory-one strategies.

Cooperative equilibria of strategy-form games with both nontransferable and transferable utilities

In this paper, we consider a class of strategy-form games with both nontransferable and transferable utilities. Inspired by NTU and TU α-core concepts, we first introduce the notion of cooperative equilibria, and prove the existence theorem in this model with finite dimensional strategy spaces. Furthermore, we extend the cooperative equilibrium existence theorem to strategy-form games with infinite dimensional strategy spaces.