The Prisoner's Dilemma in the Workplace: How Cooperative Behavior of Managers Influence Organizational Performance and Stress

The aim of the paper is to analyze the impact of cooperativeness of managers who occupy central positions in interaction networks on the performance and stress levels of a whole organization. To explore this relationship, a multi-parameter agent-based model is proposed which implements the Prisoner's Dilemma Game approach on a scale-free network in the NetLogo environment. A description of the socio-economic aspects and the key concepts implemented in the model are provided. Stability and correctness have been tested through a series of validation experiments, including sensitivity analysis. The source code is available for further exploration and testing. The simulations revealed that improving the stress resistance of all employees moderately increases organizational performance. Analyzing managers' roles showed that increasing only the stress resistance of managers does not account for significantly higher overall performance. However, a substantial increase in organizational performance and a decrease in stress levels are achieved when managers are unconditionally cooperative. This effect is stronger for the lowered stress resistance of employees. Therefore, the willingness of managers to cooperate under all circumstances can be a key factor in achieving better performance and building a more pleasant, stress-free working environment. This paper presents a model for analyzing cooperation, specifically in the organizational context, extending the Prisoner's Dilemma with novel concepts and mechanisms. While the results confirm the existing theories about the importance of central nodes in complex networks, they also provide further details on how the cooperative behavior of central nodes (i.e., the managers) might benefit the organization

Studies on consumers’ benefits from transformation of electricity markets

電力市場におけるサービスを改善するために、各国で電力市場の構造改革が行われている。消費者への電力料金の低廉化を実現するために、多くの国で電力小売市場の規制緩和が行われている。一方で、再生可能エネルギー資源の活用が世界的に進んでいる。電力市場の変革に伴い、新たな取引形態を期待できるが、市場の自由化は消費者に影響を及ぼす多くの問題を孕んでいる。本論文では、電力市場を活性化させるために、電力市場における消費者の効用を分析するモデルの確立を目的としている。消費者の効用を分析するために４つの問題を設定し、グラフ理論に基づいた数理科学的手法を用いて新たな市場モデルを提案している。研究の結果、提案モデルにおける消費者の効用分析を通して、消費者の意思決定や振る舞いの特徴と効用との関係を明確にすることができ、電力市場の活性化を阻む状況を改善するいくつかの洞察が提案されている。本論文は6 章から構成されている。第1 章では、世界的に進められている電力市場の自由化などの構造改革の状況に関して、根源的な問題点を提起している。具体的には、構造改革における新たな形態の電力市場の設計では、電力取引において消費者が得られる便益を考慮しなければ、本来期待していた構造改革の結果が得られない可能性があるとの仮説を立てている。そして、関連研究や既存技術との比較から、構造改革から消費者にもたらされる便益を分析することの重要性を説明し、本研究の位置付けを明示している。第2 章では、本研究において消費者の便益を分析するための基盤となる概念として、電力市場の数理モデル構築のための理論的な枠組みを提案している。上記の枠組みでは、数理モデルで表現する内容として、電力市場の参加者間の取引における構造、電力の需要と供給の一致、電力取引の便益を数量的に表現する効用の概念、の3 点に着目している。そして、グラフ理論におけるマッチングやネットワークフローなどの手法に基づいて、数理モデルを構築するための考え方を説明している。第3 章では、自由化された電力市場における、市場参加者の効用の総和である社会的効用を最大化する取引の実現について述べられている。前章の内容に基づき、電力の需要と供給を満たしつつ社会的効用を最大化する取引の組み合わせをマッチングとして定義し、マッチングを算出するための最適化問題を定式化している。そして、シミュレーション実験から算出した電力取引および消費者の効用と、現実の電力市場で観測される状況とのギャップを考察し、本章で定義した数理モデルの改善点を示している。第4 章では、安価な電力を提供する供給者に切り替える消費者が少ない現実の状況に着目し、消費者の供給者切り替えの促進について述べられている。第2 章の効用の定義に加えて、消費者が電力供給者を切り替える際の障壁の1 つとされるスイッチングコストの概念を数理モデルに導入した上で、消費者の行動を反映したマッチングを算出するためにグラフ理論と進化ゲーム理論を組み合わせた手法を提案している。シミュレーション実験の結果から、消費者の供給者切り替えを促進するための条件に関して、消費者間のネットワークにおける接続関係の観点から考察している。第5 章では、電力供給が可能な消費者であるプロシューマが余剰電力を共有する状況を想定し、プロシューマ間での効用の公平性の実現について述べられている。関連研究でリソース共有の不公平性を表す概念として提案されているEnvy の概念を発展させ、共有対象の電力の量が刻々と変化する状況におけるプロシューマ間のマッチングにおけるEnvy を定義し、第2 章で定義した数理モデルを拡張している。互いに知り合いである消費者間での電力共有を想定したシミュレーション実験の結果から、プロシューマ間のEnvy の低減に向けて重要な条件を、消費者間のネットワーク構造の観点から考察している。最後に第6 章では、結論がまとめられており、残された課題、および今後の研究の展望について述べられている。創価大

Mediating skills on risk management for improving the resilience of Supply Networks by developing and using a serious game

Given their importance, the need for resilience and the management of risk within Supply Networks, means that engineering students need a solid under-standing of these issues. An innovative way of meeting this need is through the use of serious games. Serious games allow an active experience on how differ-ent factors influencethe flexibility, vulnerability and capabilities in Supply Networks and allow the students to apply knowledge and methods acquired from theory. This supports their ability to understand, analyse and evaluate how different factors contribute to the resilience. The experience gained within the game will contribute to the studentsâ abilities to construct new knowledge based on their active observation and reflection of the environment when they later work in a dynamic environment in industry. This game, Beware, was developed for use in a blended learning environment. It is a part of a course for engineering master students at the University of Bremen. It was found that the game was effective in mediating the topic of risk management to the students espscially in supporting their ability of applying methods, analyse the different interactions and the game play as well as to support the assessment of how their decision-making affected the simulated network

Deep multiagent reinforcement learning: challenges and directions

This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players' joint actions and (b) the computational complexity increases. We present the most common multiagent problem representations and their main challenges, and identify five research areas that address one or more of these challenges: centralised training and decentralised execution, opponent modelling, communication, efficient coordination, and reward shaping. We find that many computational studies rely on unrealistic assumptions or are not generalisable to other settings; they struggle to overcome the curse of dimensionality or nonstationarity. Approaches from psychology and sociology capture promising relevant behaviours, such as communication and coordination, to help agents achieve better performance in multiagent settings. We suggest that, for multiagent RL to be successful, future research should address these challenges with an interdisciplinary approach to open up new possibilities in multiagent RL.Algorithms and the Foundations of Software technolog

Performance evaluation of cooperation strategies for m-health services and applications

Health telematics are becoming a major improvement for patients’ lives, especially for disabled, elderly, and chronically ill people. Information and communication technologies have rapidly grown along with the mobile Internet concept of anywhere and anytime connection. In this context, Mobile Health (m-Health) proposes healthcare services delivering, overcoming geographical, temporal and even organizational barriers. Pervasive and m-Health services aim to respond several emerging problems in health services, including the increasing number of chronic diseases related to lifestyle, high costs in existing national health services, the need to empower patients and families to self-care and manage their own healthcare, and the need to provide direct access to health services, regardless the time and place. Mobile Health (m- Health) systems include the use of mobile devices and applications that interact with patients and caretakers. However, mobile devices have several constraints (such as, processor, energy, and storage resource limitations), affecting the quality of service and user experience. Architectures based on mobile devices and wireless communications presents several challenged issues and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, and network delays. In this sense, cooperation-based approaches are presented as a solution to solve such limitations, focusing on increasing network connectivity, communication rates, and reliability. Cooperation is an important research topic that has been growing in recent years. With the advent of wireless networks, several recent studies present cooperation mechanisms and algorithms as a solution to improve wireless networks performance. In the absence of a stable network infrastructure, mobile nodes cooperate with each other performing all networking functionalities. For example, it can support intermediate nodes forwarding packets between two distant nodes. This Thesis proposes a novel cooperation strategy for m-Health services and applications. This reputation-based scheme uses a Web-service to handle all the nodes reputation and networking permissions. Its main goal is to provide Internet services to mobile devices without network connectivity through cooperation with neighbor devices. Therefore resolving the above mentioned network problems and resulting in a major improvement for m-Health network architectures performances. A performance evaluation of this proposal through a real network scenario demonstrating and validating this cooperative scheme using a real m-Health application is presented. A cryptography solution for m-Health applications under cooperative environments, called DE4MHA, is also proposed and evaluated using the same real network scenario and the same m-Health application. Finally, this work proposes, a generalized cooperative application framework, called MobiCoop, that extends the incentive-based cooperative scheme for m-Health applications for all mobile applications. Its performance evaluation is also presented through a real network scenario demonstrating and validating MobiCoop using different mobile applications

Artificial Intelligence based multi-agent control system

Le metodologie di Intelligenza Artificiale (AI) si occupano della possibilità di rendere le macchine in grado di compiere azioni intelligenti con lo scopo di aiutare l’essere umano; quindi è possibile affermare che l’Intelligenza Artificiale consente di portare all’interno delle macchine, caratteristiche tipiche considerate come caratteristiche umane. Nello spazio dell’Intelligenza Artificiale ci sono molti compiti che potrebbero essere richiesti alla macchina come la percezione dell’ambiente, la percezione visiva, decisioni complesse. La recente evoluzione in questo campo ha prodotto notevoli scoperte, princi- palmente in sistemi ingegneristici come sistemi multi-agente, sistemi in rete, impianti, sistemi veicolari, sistemi sanitari; infatti una parte dei suddetti sistemi di ingegneria è presente in questa tesi di dottorato. Lo scopo principale di questo lavoro è presentare le mie recenti attività di ricerca nel campo di sistemi complessi che portano le metodologie di intelligenza artifi- ciale ad essere applicati in diversi ambienti, come nelle reti di telecomunicazione, nei sistemi di trasporto e nei sistemi sanitari per la Medicina Personalizzata. Gli approcci progettati e sviluppati nel campo delle reti di telecomunicazione sono presentati nel Capitolo 2, dove un algoritmo di Multi Agent Reinforcement Learning è stato progettato per implementare un approccio model-free al fine di controllare e aumentare il livello di soddisfazione degli utenti; le attività di ricerca nel campo dei sistemi di trasporto sono presentate alla fine del capitolo 2 e nel capitolo 3, in cui i due approcci riguardanti un algoritmo di Reinforcement Learning e un algoritmo di Deep Learning sono stati progettati e sviluppati per far fronte a soluzioni di viaggio personalizzate e all’identificazione automatica dei mezzi trasporto; le ricerche svolte nel campo della Medicina Personalizzata sono state presentate nel Capitolo 4 dove è stato presentato un approccio basato sul controllo Deep Learning e Model Predictive Control per affrontare il problema del controllo dei fattori biologici nei pazienti diabetici.Artificial Intelligence (AI) is a science that deals with the problem of having machines perform intelligent, complex, actions with the aim of helping the human being. It is then possible to assert that Artificial Intelligence permits to bring into machines, typical characteristics and abilities that were once limited to human intervention. In the field of AI there are several tasks that ideally could be delegated to machines, such as environment aware perception, visual perception and complex decisions in the various field. The recent research trends in this field have produced remarkable upgrades mainly on complex engineering systems such as multi-agent systems, networked systems, manufacturing, vehicular and transportation systems, health care; in fact, a portion of the mentioned engineering system is discussed in this PhD thesis, as most of them are typical field of application for traditional control systems. The main purpose if this work is to present my recent research activities in the field of complex systems, bringing artificial intelligent methodologies in different environments such as in telecommunication networks, transportation systems and health care for Personalized Medicine. The designed and developed approaches in the field of telecommunication net- works is presented in Chapter 2, where a multi-agent reinforcement learning algorithm was designed to implement a model-free control approach in order to regulate and improve the level of satisfaction of the users, while the research activities in the field of transportation systems are presented at the end of Chapter 2 and in Chapter 3, where two approaches regarding a Reinforcement Learning algorithm and a Deep Learning algorithm were designed and developed to cope with tailored travels and automatic identification of transportation moralities. Finally, the research activities performed in the field of Personalized Medicine have been presented in Chapter 4 where a Deep Learning and Model Predictive control based approach are presented to address the problem of controlling biological factors in diabetic patients

The Gamification of Crowdsourcing Systems: Empirical Investigations and Design

Recent developments in modern information and communication technologies have spawned two rising phenomena, gamification and crowdsourcing, which are increasingly being combined into gamified crowdsourcing systems. While a growing number of organizations employ crowdsourcing as a way to outsource tasks related to the inventing, producing, funding, or distributing of their products and services to the crowd – a large group of people reachable via the internet – crowdsourcing initiatives become enriched with design features from games to motivate the crowd to participate in these efforts. From a practical perspective, this combination seems intuitively appealing, since using gamification in crowdsourcing systems promises to increase motivations, participation and output quality, as well as to replace traditionally used financial incentives. However, people in large groups all have individual interests and motivations, which makes it complex to design gamification approaches for crowds. Further, crowdsourcing systems exist in various forms and are used for various tasks and problems, thus requiring different incentive mechanisms for different crowdsourcing types. The lack of a coherent understanding of the different facets of gamified crowdsourcing systems and the lack of knowledge about the motivational and behavioral effects of applying various types of gamification features in different crowdsourcing systems inhibit us from designing solutions that harness gamification’s full potential. Further, previous research canonically uses competitive gamification, although crowdsourcing systems often strive to produce cooperative outcomes. However, the potentially relevant field of cooperative gamification has to date barely been explored. With a specific focus on these shortcomings, this dissertation presents several studies to advance the understanding of using gamification in crowdsourcing systems