Strategy Space Research Articles

Strategic fit revelation with advertising

Consumers are often uncertain about how a product fits their individual preferences before purchase. To resolve such uncertainty, firms can use fit-revelation activities such as sampling, product demonstration, and virtual showrooms. Recently, there is an emerging trend of combining product sampling with advertising (e.g., Amazon’s sampling-advertising initiative). Empirical research has shown that fit-revelation may enhance or weaken the effect of advertising. We contribute to the literature by developing an analytical model that links these interactions with the firm’s strategic decisions. We find that, interestingly, when fit-revelation enhances the effect of advertising, these two instruments are not necessarily complementary. Our study further characterizes the firm’s strategy space of whether to conduct fit-revelation alone, advertising alone, or both. We show that advertising may reverse the firm’s decision on whether to facilitate fit-revelation. Moreover, how complementarity/substitutability affects the optimal strategy depends on the types of ads. While substitutability denies the optimality of the joint strategy under informative ads, it fails to do so under persuasive ads. On the other hand, complementarity is necessary for the joint strategy to be optimal under informative ads, which is not the case under persuasive ads. Finally, we extend our analysis to targeted fit-revelation/advertising with heterogeneous consumers ex ante, in which case the firm may find it optimal to target low-valuation consumers. Given the latest trends and growing interest in the deployment of joint advertising and sampling, our study offers timely insights for practitioners.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

A stochastic variant of replicator dynamics in zero-sum games and its invariant measures

We study the behavior of a stochastic variant of replicator dynamics in two-agent zero-sum games. Inspired by the well studied deterministic replicator equation, this model gives arguably a more realistic description of real world settings and, as we demonstrate here, exhibits totally distinct behavior when compared to its deterministic counterpart which is known to be recurrent. In more detail, we characterize the statistics of such systems by their invariant measures which can be shown to be entirely supported on the boundary of the space of mixed strategies. Depending on the noise strength we can furthermore characterize these invariant measures by finding accumulation of mass at specific parts of the boundary. In particular, regardless of the magnitude of noise, we show that any invariant probability measure is a convex combination of Dirac measures on pure strategy profiles, which correspond to vertices/corners of the agents’ simplices. Thus, in the presence of stochastic perturbations, even in the most classic zero-sum settings, such as Matching Pennies, we observe a stark disagreement between the axiomatic prediction of Nash equilibrium and the evolutionary emergent behavior derived by an assumption of stochastically adaptive, learning agents.

Whole-Body Dynamics-Based Aerial Fall Trajectory Optimization and Landing Control for Humanoid Robot.

When humanoid robots work in human environments, falls are inevitable due to the complexity of such environments. Current research on humanoid robot falls has mainly focused on falls on the ground, with little research on humanoid robots falling from the air. In this paper, we employ an extended state variable formulation that directly maps from the high-level motion strategy space to the full-body joint space to optimize the falling trajectory in order to protect the robot when falling from the air. In order to mitigate the impact force generated by the robot's fall, during the aerial phase, we employ simple proportion differentiation (PD) control. In the landing phase, we optimize the optimal contact force at the contact point using the centroidal dynamics model. Based on the contact force, the changes to the end-effector positions are solved using a dual spring-damper model. In the simulation experiments, we conduct three comparative experiments, and the simulation results demonstrate that the robot can safely fall 1.5 m from the ground at a pitch angle of 45°. Finally, we experimentally validate the methods on an actual robot by performing a side-fall experiment. The experimental results show that the proposed trajectory optimization and motion control methods can provide excellent shock absorption for the impact generated when a robot falls.

Polarize, catalyze, and stabilize: how a minority of norm internalizers amplify group selection and punishment

Many mechanisms behind the evolution of cooperation, such as reciprocity, indirect reciprocity, and altruistic punishment, require group knowledge of individual actions. But what keeps people cooperating when no one is looking? Conformist norm internalization, the tendency to abide by the behavior of the majority of the group, even when it is individually harmful, could be the answer. In this paper, we analyze a world where (1) there is group selection and punishment by indirect reciprocity, but (2) many actions (half) go unobserved and therefore unpunished. Can norm internalization fill this “observation gap” and lead to high levels of cooperation, even when agents may in principle cooperate only when likely to be caught and punished? Specifically, we seek to understand whether adding norm internalization to the strategy space in a public goods game can lead to higher levels of cooperation when both norm internalization and cooperation start out rare. We found the answer to be positive, but, interestingly, not because norm internalizers end up making up a substantial fraction of the population, nor because they cooperate much more than other agent types. Instead, norm internalizers, by polarizing, catalyzing, and stabilizing cooperation, can increase levels of cooperation of other agent types, while only making up a minority of the population themselves.Significance statementWhat keeps people cooperating when no one is looking? Group selection favoring cooperative groups does not require observers, but often works best when there is some other norm enforcement mechanism to supplement it. But most norm enforcement mechanisms require observers in order to function. Here, we examine whether norm internalization could fill this enforcement gap, by acting as an “internal enforcer” of norms. Perhaps surprisingly, we discovered in our simulations that the population of norm internalizers always remained quite small, and norm internalizers did not necessarily cooperate more than other agent types. Nevertheless, under certain conditions, they were able to spark significantly higher mean levels of cooperation, by causing other agents to cooperate more—partly by setting off bouts of high cooperation after periods of very low cooperation, and partly by helping maintain those bouts of high cooperation for longer.

Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

A deep learning based sensor fusion method to diagnose tightening errors

Modern smart assembly lines commonly include electric tools with built-in sensors to tighten safety-critical joints. These sensors generate data that are subsequently analyzed by human experts to diagnose potential tightening errors. Previous research aimed to automate the diagnosing process by developing diagnosing models based on tightening theory and calibration of the friction coefficient in specific lab setups. Generalizing these results is difficult and often unsuccessful since friction coefficients vary between lab and production environments. To overcome this problem, this paper presents a novel methodology that builds multi-label classification deep learning models for diagnosing tightening errors using production data. The proposed methodology comprises three key contributions, i.e., the Labrador method, the Model Combo (MoBo) framework, and a heuristic evaluation method. Labrador is an elastic deep learning based sensor fusion method that (1) uses feature encoders to extract features; (2) conducts data-level and/or feature-level sensor fusion in both time and frequency domains; and (3) performs multi-label classification to detect and diagnose tightening errors. MoBo is a configurable and modular framework that supports Labrador in identifying optimal feature encoders. With MoBo and Labrador, one can easily explore and design a bounded search space for sensor fusion strategies (SFSs) and feature encoders. In order to identify the optimal solution within the defined search space, this paper introduces a heuristic method. By evaluating the trade-off between machine learning (ML) metrics (e.g., accuracy, subset accuracy, and F1) and operational (OP) metrics (e.g., inference latency), the proposed method identifies the most suitable solution depending on the requirements of individual use cases. In the experimental evaluation, we adopt the proposed methodology to identify the most suitable multi-label classification solutions for diagnosing tightening errors. To optimize ML metrics, the identified solution achieved 99.69% accuracy, 93.39% subset accuracy, 97.39% F1, and 6.68ms inference latency. To optimize OP metrics, the identified solution achieved 99.66% accuracy, 92.65% subset accuracy, 97.28% F1, and 2.41ms inference latency.

Developmental changes in exploration resemble stochastic optimization

Human development is often described as a ‘cooling off’ process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.

Digital self-help for people experiencing intimate partner violence: a qualitative study on user experiences and needs including people with lived experiences and services providers

BackgroundIntimate partner violence (IPV) is a prevalent public health issue associated with multiple physical and mental health consequences for survivors. Digital interventions can provide low-threshold support to those experiencing IPV, but existing digital interventions have limited efficacy in improving the safety and mental health of IPV survivors. Digitally adapting an integrative intervention with advocacy-based and psychological content holds promise for increasing the efficacy of digital interventions in the context of IPV.MethodsThis study examines the needs, acceptability and usability of an integrative digital intervention for people affected by IPV. We used the think-aloud method and semi-structured interviews with a sample of six people with lived experiences of IPV and six service providers. We analyzed the data using thematic analysis.ResultsWe identified the increasing general acceptance of digital support tools and the limited capacity of the current support system as societal context factors influencing the acceptance of and needs regarding digital interventions in the context of IPV. An integrative digital self-help intervention offers several opportunities to complement the current support system and to meet the needs of people affected by IPV, including the reduction of social isolation, a space for self-reflection and coping strategies to alleviate the situation. However, potentially ongoing violence, varying stages of awareness and psychological capacities, and as well as the diversity of IPV survivors make it challenging to develop a digital intervention suitable for the target group. We received feedback on the content of the intervention and identified design features required for intervention usability.ConclusionAn integrative digital self-help approach, with appropriate security measures and trauma-informed design, has the potential to provide well-accepted, comprehensive and continuous psychosocial support to people experiencing IPV. A multi-modular intervention that covers different topics and can be personalized to individual user needs could address the diversity of the target population. Providing guidance for the digital intervention is critical to spontaneously address individual needs. Further research is needed to evaluate the efficacy of an integrative digital self-help intervention and to explore its feasibility it in different settings and populations.

Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation

<p>To enhance the smart Cloud Service System for diverse user requirements in 5G and other service networks, this study leverages resource utilization and multi-tenancy network slicing operation costs. Specifically, we propose a multi-tenancy network resource allocation strategy based on the Chaos Particle Swarm Optimization (CPSO) algorithm. In a multi-tenancy network (MTN), we lease the wireless spectrum resources of the infrastructure provider’s base station, construct access service slices as network slice services, and offer network access services to users. Introduce detailed formulation of the relationship between MTN and users, represented as a multi-master and multi-slave construct that defines the strategy space and profit function after MTN decision-making. Reverse induction is used to analyze the proposed model, and a distributed iterative algorithm is proposed to obtain the optimal throughput demand of users and the optimal slice cost of MTN. Simulation results demonstrate that the proposed strategy can effectively enhance resource utilization and user satisfaction while reducing energy consumption.</p>

Development of Extended-Release Formulations Containing Cyclobenzaprine Based on Physiologically Based Biopharmaceutics Modeling and Bioequivalence Safe Space

The use of physiologically based biopharmaceutics modeling (PBBM) and bioequivalence safe space is increasingly common for immediate-release drug products. However, for extended-release (ER) formulations there are only a few examples of this application. In this study, we developed ER formulations containing cyclobenzaprine 15 mg, supported by PBBM and bioequivalence safe space. Four formulations were prepared, F1, F2, F3 (ER mini-tablet formulations) and F4 (ER tablet formulation), and the dissolution profiles were evaluated. The dissolution profile of the reference drug product was also evaluated and used to set a bioequivalence safe space. A PBBM was set up, evaluated, and used to predict the in vivo behavior of the formulations. The bioequivalence safe space was calculated to be between – 25% and + 75% of the k1 and Tlag values of the dissolution profile of the reference drug product when applying the first-order dissolution kinetic model. All time points of the dissolution profile of the ER mini-tablet formulation F2, were within the safe space, and was approved in 10 of 10 trials of crossover virtual bioequivalence studies. Based on the PBBM strategy and bioequivalence safe space, it was possible to develop an ER mini-tablet formulation virtually bioequivalent to the reference drug product, even though this formulation failed the f2 test.

Best-Response Dynamics for Evolutionary Stochastic Games

We introduce a version of best-response (BR) dynamics for stochastic games using the framework of evolutionary stochastic games proposed in [Flesch, J., Parthasarathy, T., Thuijsman, F. and Uyttendaele, P. [2013] Evolutionary stochastic games, Dyn. Games Appl. 3, 207–219]. This dynamics is defined on the simplex of all population distributions, as in classical evolutionary games. In this BR dynamics, it is shown that the set of all population distributions inducing Nash equilibria is globally asymptotically stable, for stable stochastic games. Also, if we transform the BR dynamics into the space of stationary strategies, the set of all Nash Equilibria is asymptotically stable. Another version of BR dynamics is defined in the space of stationary strategies for zero-sum stochastic games by [Leslie, D. S., Perkins, S. and Xu, Z. [2020] Best-response dynamics in zero-sum stochastic games, J. Econ. Theory 189, 105095]. To compare both versions of BR dynamics, we extend the evolutionary stochastic games model to asymmetric stochastic games and employ an equivalent definition of Nash equilibria. This comparison is illustrated using examples and it is observed that the trajectories of both versions of BR dynamics do converge to the same Nash equilibrium. The BR dynamics proposed in this paper has the advantage of being valid for nonzero sum stochastic games and conforms to both classical game dynamics and the dynamics of stationary strategies in stochastic games.

Efficient computation of discrete games: Estimating the effect of Apple on market structure

Discrete games provide the means to analyze market dynamics with limited data. However, computing such games with many players—especially in a complete information setting—is computationally infeasible because the strategy space increases exponentially with the number of players. This study presents a novel and practical method to compute and estimate discrete games. To do so, the study introduces two methodological innovations. First, we develop an efficient simulator that requires fewer random draws to evaluate the likelihood of discrete games with multiple equilibria. The augmented simulator avoids random draws that are not compatible with the observed equilibrium outcome and, thus, efficiently uses all draws to evaluate the likelihood. Second, we utilize general‐purpose computing on graphics‐processing unit (GPGPU), using multiple processing cores in a graphics‐processing unit, to increase computational speed. The two features allow us to estimate the model significantly faster compared to traditional methods. The study's empirical application examines the effect of Apple's company‐owned stores on the retail market structure. The results show that agglomeration effects exist between Apple and upscale firms. The presence of an Apple store attracts high‐income customers, promoting the entry of upscale firms and the exit of discount firms.

Adaptive dynamics of memory-one strategies in the repeated donation game.

Human interactions can take the form of social dilemmas: collectively, people fare best if all cooperate but each individual is tempted to free ride. Social dilemmas can be resolved when individuals interact repeatedly. Repetition allows them to adopt reciprocal strategies which incentivize cooperation. The most basic model for direct reciprocity is the repeated donation game, a variant of the prisoner's dilemma. Two players interact over many rounds; in each round they decide whether to cooperate or to defect. Strategies take into account the history of the play. Memory-one strategies depend only on the previous round. Even though they are among the most elementary strategies of direct reciprocity, their evolutionary dynamics has been difficult to study analytically. As a result, much previous work has relied on simulations. Here, we derive and analyze their adaptive dynamics. We show that the four-dimensional space of memory-one strategies has an invariant three-dimensional subspace, generated by the memory-one counting strategies. Counting strategies record how many players cooperated in the previous round, without considering who cooperated. We give a partial characterization of adaptive dynamics for memory-one strategies and a full characterization for memory-one counting strategies.

Solving Large-Scale Pursuit-Evasion Games Using Pre-trained Strategies

Pursuit-evasion games on graphs model the coordination of police forces chasing a fleeing felon in real-world urban settings, using the standard framework of imperfect-information extensive-form games (EFGs). In recent years, solving EFGs has been largely dominated by the Policy-Space Response Oracle (PSRO) methods due to their modularity, scalability, and favorable convergence properties. However, even these methods quickly reach their limits when facing large combinatorial strategy spaces of the pursuit-evasion games. To improve their efficiency, we integrate the pre-training and fine-tuning paradigm into the core module of PSRO -- the repeated computation of the best response. First, we pre-train the pursuer's policy base model against many different strategies of the evader. Then we proceed with the PSRO loop and fine-tune the pre-trained policy to attain the pursuer's best responses. The empirical evaluation shows that our approach significantly outperforms the baselines in terms of speed and scalability, and can solve even games on street maps of megalopolises with tens of thousands of crossroads -- a scale beyond the effective reach of previous methods.

On Tsirelson pairs of C*-algebras

We introduce the notion of a Tsirelson pair of C*-algebras, which is a pair of C*-algebras for which the space of quantum strategies obtained by using states on the minimal tensor product of the pair is dense in the space of quantum strategies obtained by using states on the maximal tensor product. We exhibit a number of examples of such pairs that are “nontrivial” in the sense that the minimal tensor product and the maximal tensor product of the pair are not isomorphic. For example, we prove that any pair containing a C*-algebra with Kirchberg’s QWEP property is a Tsirelson pair. We then introduce the notion of a C*-algebra with the Tsirelson property (TP) and establish a number of closure properties for this class. We also show that the class of C*-algebras with the TP forms an elementary class (in the sense of model theory), but that this class does not admit an effective axiomatization.

Grouping promotes both partnership and rivalry with long memory in direct reciprocity.

Biological and social scientists have long been interested in understanding how to reconcile individual and collective interests in the iterated Prisoner's Dilemma. Many effective strategies have been proposed, and they are often categorized into one of two classes, 'partners' and 'rivals.' More recently, another class, 'friendly rivals,' has been identified in longer-memory strategy spaces. Friendly rivals qualify as both partners and rivals: They fully cooperate with themselves, like partners, but never allow their co-players to earn higher payoffs, like rivals. Although they have appealing theoretical properties, it is unclear whether they would emerge in an evolving population because most previous works focus on the memory-one strategy space, where no friendly rival strategy exists. To investigate this issue, we have conducted evolutionary simulations in well-mixed and group-structured populations and compared the evolutionary dynamics between memory-one and longer-memory strategy spaces. In a well-mixed population, the memory length does not make a major difference, and the key factors are the population size and the benefit of cooperation. Friendly rivals play a minor role because being a partner or a rival is often good enough in a given environment. It is in a group-structured population that memory length makes a stark difference: When longer-memory strategies are available, friendly rivals become dominant, and the cooperation level nearly reaches a maximum, even when the benefit of cooperation is so low that cooperation would not be achieved in a well-mixed population. This result highlights the important interaction between group structure and memory lengths that drive the evolution of cooperation.

Mitigating moral hazard with usage‐based insurance

AbstractTechnological progress has improved insurers' ability to monitor policyholders and has led to usage‐based insurance (UBI) contracts that incorporate behavioral risk factors in pricing. Economic theory predicts that any informative monitoring signal is adopted in equilibrium. In practice, the demand for UBI is still low to date with market shares in the single digits. We modify the standard moral‐hazard model in insurance economics by trading off a simpler effort model for a richer strategy space, and by focusing on the use of monitoring for premium differentiation. In our model, an informative monitoring technology is in use if it is sufficiently accurate. Otherwise, the premium incentive from monitoring is not large enough to alleviate the incentive‐compatibility constraint to an extent that would make policyholders better off. Our results help explain the slow adoption of UBI contracts in practice and provide an avenue to increase their appeal to policyholders.

Deep Reinforcement Learning for Shared Offloading Strategy in Vehicle Edge Computing

Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by offloading computing tasks from vehicle terminals to edge servers. However, offloading of tasks increase in quantity the transmission time and energy of the network. In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted. The shared offloading strategy can be adapted to the complex scenarios of the IoVs. Each vehicle can share the offloading conditions of the VEC servers, and then adaptively select three computing modes: local execution, task offloading, and shared offloading. In this article, the network state and offloading strategy space are the input of the deep reinforcement learning (DRL). Through the DRL, each task unit selects the offloading strategy with the optimal energy consumption at each time period in the dynamic IoVs transmission and computing environment. Compared with the existing proposals and DRL-based algorithms, it can effectively reduce the delay and energy consumption required for tasks offloading.

Score-mediated mutual consent and indirect reciprocity

Helping strangers at a cost to oneself is a hallmark of many human interactions, but difficult to justify from the viewpoint of natural selection, particularly in anonymous one-shot interactions. Reputational scoring can provide the necessary motivation via "indirect reciprocity," but maintaining reliable scores requires close oversight to prevent cheating. We show that in the absence of such supervision, it is possible that scores might be managed by mutual consent between the agents themselves instead of by third parties. The space of possible strategies for such "consented" score changes is very large but, using a simple cooperation game, we search it, asking what kinds of agreement can i) invade a population from rare and ii) resist invasion once common. We prove mathematically and demonstrate computationally that score mediation by mutual consent does enable cooperation without oversight. Moreover, the most invasive and stable strategies belong to one family and ground the concept of value by incrementing one score at the cost of the other, thus closely resembling the token exchange that underlies money in everyday human transactions. The most successful strategy has the flavor of money except that agents without money can generate new score if they meet. This strategy is evolutionarily stable, and has higher fitness, but is not physically realizable in a decentralized way; when conservation of score is enforced more money-like strategies dominate. The equilibrium distribution of scores under any of this family of strategies is geometric, meaning that agents with score 0 are inherent to money-like strategies.