Computing Effective Mixed Strategies for Protecting Targets in Large-Scale Critical Infrastructure Networks

Most critical infrastructure networks often suffer malicious attacks, which may result in network failures. Therefore, how to design more robust defense measures to minimize the loss is a great challenge. In recent years, defense strategies for enhancing the robustness of the networks are developed based on the game theory. However, the aforementioned method cannot effectively solve the defending problem on large-scale networks with a full strategy space. In this study, we achieve the purpose of protecting the infrastructure networks by allocating limited resources to monitor the targets. Based on the existing two-person zero-sum game model and the Double Oracle framework, we propose the EMSL algorithm which is an approximation algorithm based on a greedy search to compute effective mixed strategies for protecting large-scale networks. The improvement of our approximation algorithm to other algorithms is discussed. Experimental results show that our approximation algorithm can efficiently compute the mixed strategies on actual large-scale networks with a full strategy space, and the mixed defense strategies bring the highest utility to a defender on different networks when dealing with different attacks.

Complex eco-evolutionary dynamics induced by the coevolution of predator–prey movement strategies

The coevolution of predators and prey has been the subject of much empirical and theoretical research that produced intriguing insights into the interplay of ecology and evolution. To allow for mathematical analysis, models of predator–prey coevolution are often coarse-grained, focussing on population-level processes and largely neglecting individual-level behaviour. As selection is acting on individual-level properties, we here present a more mechanistic approach: an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape, where features relevant for ecology (like changes in local densities) and evolution (like differences in survival and reproduction) emerge naturally from interactions between individuals. Our focus is on predator–prey movement behaviour, and we present a new method for implementing evolving movement strategies in an efficient and intuitively appealing manner. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of their movement strategies. Over the generations, the movement strategies evolve, as individuals that successfully survive and reproduce leave their strategy to more descendants. We show that the movement strategies in our model evolve rapidly, thereby inducing characteristic spatial patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by antagonistic coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies within the same population. Although the strategy space of our model is continuous, we often observe the evolution of discrete movement types. We argue that rapid evolution, coexistent movement types, and phase shifts between different ecological regimes are not a peculiarity of our model but a result of more realistic assumptions on eco-evolutionary feedbacks and the number of evolutionary degrees of freedom.

On the Effect of Practice on Exploration and Exploitation of Options and Strategies

Insufficient exploration of one’s surroundings is at the root of many real-life problems, as demonstrated by many famous biases (e.g., the status quo bias, learned helplessness). The current work focuses on the emergence of this phenomenon at the strategy level: the tendency to under-explore the set of available choice strategies. We demonstrate that insufficient exploration of strategies can also manifest as excessive exploration between options. In such cases, interventions aimed at improving choices by reducing the costs of exploration of options are likely to fail. In Study 1, participants faced an exploration task that implies an infinite number of choice strategies and a small sub-set of (near) optimal solutions. We manipulated the amount of practice participants underwent during the first, shorter game and compared their performance in a second, longer game with an identical payoff structure. Our results show that regardless of the amount of practice, participants in all experimental groups tended to under-explore the strategy space and relied on a specific strategy that implied over-exploration of the option space. That is, under-exploration of strategies was manifested as over-exploration of options. In Study 2, we added a constraint that, on a subset of practice trials, forced participants to exploit familiar options. This manipulation almost doubled the per-trial average outcome on the comparable longer second game. This suggests that forcing participants to experience the effects of different (underexplored) strategy components during practice can greatly increase the chance they make better choices later on.

An Equilibrium Analysis of a Secondary Mobile Data-Share Market

Internet service providers are offering shared data plans where multiple users may buy and sell their overage data in a secondary market managed by the ISP. We propose a game-theoretic approach to a software-defined network for modeling this wireless data exchange market: a fully connected, non-cooperative network. We identify and define the rules for the underlying progressive second price (PSP) auction for the respective network and market structure. We allow for a single degree of statistical freedom—the reserve price—and show that the secondary data exchange market allows for greater flexibility in the acquisition decision making of mechanism design. We have designed a framework to optimize the strategy space using the elasticity of supply and demand. Wireless users are modeled as a distribution of buyers and sellers with normal incentives. Our derivation of a buyer-response strategy for wireless users based on second price market dynamics leads us to prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that our network upholds the desired properties for optimization with respect to software-defined networks and prove the existence of a Nash equilibrium in the overlying non-cooperative game.

Pure Nash Equilibria in Resource Graph Games

This paper studies the existence of pure Nash equilibria in resource graph games, a general class of strategic games succinctly representing the players’ private costs. These games are defined relative to a finite set of resources and the strategy set of each player corresponds to a set of subsets of resources. The cost of a resource is an arbitrary function of the load vector of a certain subset of resources. As our main result, we give complete characterizations of the cost functions guaranteeing the existence of pure Nash equilibria for weighted and unweighted players, respectively. For unweighted players, pure Nash equilibria are guaranteed to exist for any choice of the players’ strategy space if and only if the cost of each resource is an arbitrary function of the load of the resource itself and linear in the load of all other resources where the linear coefficients of mutual influence of different resources are symmetric. This implies in particular that for any other cost structure there is a resource graph game that does not have a pure Nash equilibrium. For weighted games where players have intrinsic weights and the cost of each resource depends on the aggregated weight of its users, pure Nash equilibria are guaranteed to exist if and only if the cost of a resource is linear in all resource loads, and the linear factors of mutual influence are symmetric, or there is no interaction among resources and the cost is an exponential function of the local resource load. We further discuss the computational complexity of pure Nash equilibria in resource graph games showing that for unweighted games where pure Nash equilibria are guaranteed to exist, it is coNP-complete to decide for a given strategy profile whether it is a pure Nash equilibrium. For general resource graph games, we prove that the decision whether a pure Nash equilibrium exists is Σ p 2 -complete.

Key Technologies of Cache and Computing in 5G Mobile Communication Network

The deployment of cache and computing resources in 5G mobile communication networks is considered as an important way to reduce network transmission delay and redundant content transmission and improve the efficiency of content distribution and network computing processing capacity, which has been widely concerned and recognized by academia and industry. Aiming at the development trend of cache and computing resource allocation in 5G mobile communication networks, in order to improve the efficiency of content cache and reduce network energy consumption, a 5G network cache optimization strategy based on Stackelberg game was proposed, which modeled network operators and content providers as multimaster and multislave Stackelberg game model. Providers buy base station storage space from network operators to cache popular content. In this paper, we construct the strategy space and profit function of the two sides of the game and prove the existence of Nash equilibrium solution among content providers given a set of base station rental prices of network operators. In this paper, distributed iterative algorithm is used to solve the game model, and the optimal base station pricing of network operators and the optimal base station occupancy rate of content providers are obtained.

Analysis of a Multiparticipant Game under a Subsidy and Punishment Mechanism: An Evolutionary Theory Perspective

In a market with intense competition, cost pressures tempt enterprises to seek profits in ways that infringe on the interests of consumers. This is especially true when market sentiment is weak. In such situations, governments play a vital role in protecting consumers’ interests and helping struggling enterprises. We construct a tripartite game model that includes the government, enterprises, and consumers under a subsidy and punishment mechanism. We use this model to investigate the strategic choices made by the participants in an evolutionary game theory (EGT) framework. We present four stable equilibrium points as pure strategy solutions with the aid of a replicator dynamic system. Three main findings are presented in this paper. First, not all equilibrium points can be evolutionary stable strategies (ESSs) when considering the potential motivations of the participants to change strategies. Second, there is an equilibrium point that satisfies the stability condition but changes periodically in its strategy space; strategy changes between participants are not synchronized. Third, the government prefers to subsidize enterprises when enterprise speculation is serious or when enterprise investment in improving production technology is high.

Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing

In this paper, we study the multiobjective game in a multiconflict situation. First, the feasible strategy set and synthetic strategy space are constructed in the multiconflict situation. Meanwhile, the value of payoff function under multiobjective is determined, and an integrated multiobjective game model is established in a multiconflict situation. Second, the multiobjective game model is transformed into the single-objective game model by the Entropy Weight Method. Then, in order to solve this multiobjective game, an adaptive differential evolution algorithm based on simulated annealing (ADESA) is proposed to solve this game, which is to improve the mutation factor and crossover operator of the differential evolution (DE) algorithm adaptively, and the Metropolis rule with probability mutation ability of the simulated annealing (SA) algorithm is used. Finally, the practicability and effectiveness of the algorithm are illustrated by a military example.

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.