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In this paper, an agent-based model is employed to study urban farm interconnectivity, with the goal of increasing food availability for the population and efficiently managing food, energy and water resources. Urban farms are presented as a case study, that belong to community microgrids that generate electricity from renewables (solar, wind), in the province of Chiriqui in Panama, is utilized to investigate the food-energy-water (FEW) nexus. The idea of different farms serving different communities can be viable, however, sometimes there is a shortage of fresh food, or other times a surplus of fresh food, which leads to food wastage. Food waste has the capability of being transformed into fertilizers or into biomass energy, however when we consider the associated natural resources and costs used to produce food, avoiding waste would be a better alternative. Thus, it is crucial to investigate the food supply chain, and the interaction between different urban farms in a community shared agriculture fashion. An optimization model is employed to minimize food transportation cost between the farms within the agent-based framework. The results show that food availability is improved due to the exchange between farms while food wastage is reduced by 17,137kg. Furthermore, the simulations show that the sharing of energy through the microgrid employing renewable energy is economical and significantly reduced carbon emissions by avoiding 13,078 tonnes of CO2 and generating 16,560 Mwh of electricity through solar, and 2,333 Mwh of electricity through wind. [ABSTRACT FROM AUTHOR]

Crowds, emergencies, and traffic evacuations are examples of social systems. These complex, dynamic systems are challenging to analyze and comprehend. Two well-known techniques for modeling and simulating social systems are the agent-based (AB) model and the social force (SF) model. Whereas social force modeling focuses on the physical factors that influence how individuals move and communicate in a crowd while agent-based modeling focuses on the behavior of individual agents. Although each technique has its own advantages and disadvantages, combining them is a significant ability to capture both the social and physical components of human behavior. This work aims to combine both of these techniques, AB and SF and to capture the interactions between individual agents and the elements of the force that influence their behavior, leading to more realistic simulations of social systems. Nevertheless, there are difficulties involved in combining agentbased and social force modeling, such as choosing the appropriate system complexity and making sure that the simulation faithfully replicates the real-world system being represented. Hence, in order to overcome these obstacles and present a more precise and thorough knowledge of social systems, the creation of a conceptual framework for combining various methods is crucial. This paper presents the implementation and development of a simulation model with integrated AB and SF techniques. More complete and accurate knowledge of social systems as well as improved decision-making across a variety of disciplines is the expected outcome of combining agent-based and social force modeling. [ABSTRACT FROM AUTHOR]

The study analyzes urban energy consumption spatio-temporal patterns using agent-based modeling (ABM). The model uses the Local Indicators of Spatial Association (LISA) and the Geographically Weighted Regression (GWR) to overcome the problem of spatial heterogeneous interference in ABM simulation. In addition, the study also establishes features of preferences of various groups in energy consumption via the Decision Tree (DT). The energy consumption prediction process is executed in six modules via the transformation mechanism of living behavior and preferences of 907 agents in the studied area (Taipei City). The results of the better simulation are verified by Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result in 2030 exhibits rising trend for energy consumption in five areas, with most rising agents concentrated in downtown areas, and declining trend for energy consumption in seven areas. The cumulative distribution function (CDF) of energy consumption prediction shows the number of agents exceeding 600 KWH/household, pointing to increase in regional energy consumption. Simulation results point to trend of further concentration of energy consumption in the future. Given possible shortfall in energy supply in the future, the study suggests to build small-scale power plants or introduce energy-conservation strategies in regions with concentrated energy consumptions, to assure stability of energy demand and supply there. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the connection of objects using the internet that are equipped with sensors and communication technology. IoT Technology is a new paradigm envisioned as a global network of machines and devices capable of communicating with each other. This project presents using the agent-based model to simulate the behaviors of IoT electric meters. A case study was proposed to investigate the applicability of utilizing IoT technology in Saudi Electricity Company (SEC). The agent-based model was built using AnyLogic software. Two different scenarios were applied, and simulation outputs were analyzed to recognize the importance of adopting IoT technology at SEC. [ABSTRACT FROM AUTHOR]

Waste management is still a worldwide concern. Yogyakarta -- a city with the fourth population density rank in Indonesia, sends more than sixty thousand tons of waste to the landfill, which currently has to expand due to overloading capacity. In the last decade, the government has carried out various efforts, including promoting a waste bank program in each district. The program aims to reduce the amount of inorganic waste generated by households. Currently, the amount of waste processed is still under 20% due to a lack of participation. Thus, strategies to increase the number of people who adopt waste bank services are needed. This study aims to model the community adoption of waste bank services to obtain those strategies. An agent-based simulation approach is used by considering the behavior of sorting and selling the waste, its benefit and cost, the time for waste sorting, and the economic level. Four scenarios were evaluated, i.e., increasing the number of waste banks, reducing waste retribution charges, increasing waste incentives, and providing transportation facilities to collect waste. The results show that most of strategies can increase the adoption of the waste bank services, especially by adding more waste bank services in the residential area. [ABSTRACT FROM AUTHOR]

This study aims to connect two strands of the economics literature, behavioural corporate finance and agent-based macroeconomics, to assess the impact of managerial optimism at the micro and macro levels. More specifically, we build a macroeconomic stock-flow consistent agent-based model that is calibrated for the specific case of Poland to explore whether the optimism of top corporate managers in the context of their initial capital structure decisions is detrimental for the firms being managed in this way, the financial market dynamics, and the selected macroeconomic indicators. We model heterogeneous firms with different capital structure decision criteria depending on their degree of managerial optimism. Our model also includes a complete macroeconomic closure with aggregated households, capital producers, banking, and a public sector. We find that firms with optimistic managers outperform in terms of investment and size but are also more fragile, thereby making them more likely to default. Finally, we run policy shocks and show that while investors' flight to liquidity creates financial turmoil and increases the probability of default. Above all, for optimistic firms a good response would be to give incentives to firms to increase their liquidity margins. [ABSTRACT FROM AUTHOR]

The research presents an agent-based model that integrates a redistribution algorithm with the competition-based public blockchain reward scheme, relying on economic theories inside and outside the market. The analysis results show that pure liberalism embedded in blockchain leads to high wealth inequality, which is hard to change because the algorithm also divides the computational resource for the wealth increase. However, wealth inequality may be alleviated if the reward scheme adopts the redistribution as the policy complementing the liberal market in the modern economy. Those findings help us understand mechanisms of inequality generated by the new digital divide while calling for a more responsible blockchain protocol design to build a resilient society. [ABSTRACT FROM AUTHOR]

Growing global supply chain trends across different industries have driven enormous changes and complexity of traditional supply chain management (SCM). Simultaneously, unprecedented disruptions impacting worldwide commerce further add more vulnerabilities to current SCM practices. The most recent Covid-19 pandemic is a major global catastrophe that has created unpredictable disruptions towards supply chain activities and caused interruptions, especially in supply or sourcing activities. This low-frequency and high-impact disruption have created severe ripple effects in SCM, resulting from propagation of disturbance from upstream point of supply to downstream part of production and distribution. While traditional SCM focuses more on developing initial strategies to avoid forecasted future risks, Covid-19 phenomenon has forced SCM practitioners to propose modern SCM with agile and resilient risk management capability to adapt and respond quickly to unpredictable risk. However, it is found that only a scarce number of academic papers have suggested appropriate supply chain risk management (SCRM) solutions for the commercial sector while most addressed the issues in humanitarian SCM. Hence, this paper aims to investigate the phenomenon of supply shortage as the root problem of ripple effects in the supply chain caused by Covid-19 pandemic and propose effective SCRM to build an agile and resilient commerce supply chain for responding to the disruptions. Beyond that, the objective of this research is to also provide a clearer picture of the critical role of SCRM in the future post-pandemic world that is advantageous for supporting agile and resilient decision-making processes to turn risks into opportunities. The study first provides a comprehensive review of existing literature related to SCM and SCRM as the basis to define measurements in assessing and improving supply chain agility and resilience. Based on this preliminary study, a simulation modeling approach based on real-life case study is designed, and experimental results are presented to depict more conceptual and realistic illustrations. This paper implements a combination of agent-based and discreteevent simulation methodology with AnyLogic simulation software. Through this proposed approach, the paper finally concludes with providing managerial insights on SCRM, particularly for strategic sourcing and identifying possibilities for potential research areas. The findings and recommendations from this paper can be further exploited for advancing SCRM based on simulation technology in real-life practices as an integral part of modern supply chain capabilities. In turn, this paper contributes to help supply chain practitioners accumulate better understanding on the importance role of agile and resilient SCRM, especially in sourcing activities during disruptions period, and effective methodology for its real-world implementation that supports making investment decisions in priority settings. [ABSTRACT FROM AUTHOR]

Densely populated areas usually have problems with commuting, especially during morning rush hours. This paper presents an agent-based simulation model for real-time traffic analyses. The built simulation model is representing the part of Belgrade during morning rush hours with major traffic participants involved. The model is used to conduct simulation experiments and based on results, improvement measures are suggested. [ABSTRACT FROM AUTHOR]

The aim of the paper is to construct Agent-Based Model of tax competition based on previous works. Model consists of six countries, which compete for mobile capital by defining their policy. There are four strategies, from which country can choose its policy: to reduce or to increase one of the variables, which are tax rate and deficit ratio. Results of the simulation show that countries, in order to stay competitive, are reducing their tax rate and compensate it by increasing deficit ratio. Debt compensatory mechanism is introduced to balance whole system. Agent-Based Model is used in order to get a grasp on complexity of analyzed issue. [ABSTRACT FROM AUTHOR]

Cognitive radio technology represents one of the possible solutions to improve spectrum utilization and alleviate today's shortcomings by cognitively exploiting of unoccupied bands. It is a concept of dynamical sharing and trading of spectrum bands between participants in the spectrum market. In the spectrum market, the primary user provides services to end-users and sets the price for selling unoccupied bands. Secondary user can buy the bands and resell them to end-users. Purchasing of unoccupied bands can be considered as an investment. In this paper we aim to point out effects of changes in interest rates on behavior of all participants in the spectrum market and also impacts on the pricing strategies of operators. Analyzing results of our simulations we can conclude that interest rates have a great influence on wholesale prices but almost no impact on retail prices that seem to be stable. End-users just change the provider of services but their overall demand remains unchanged. [ABSTRACT FROM AUTHOR]

Agent-based modeling and simulation (ABMS) have been used in various fields of science with growing interest. After introduction to the approach, thought-provoking bibliometric data on ABMS usage in numerous fields is presented, including a citation network analysis. We have analyzed almost 3000 papers from Thomson Reuters Web of Science (Science Citation Index Expanded and Social Sciences Citation Index) databases in terms of publishing frequency in the observed journals for the period 2000-2015 and in terms of trends in keywords usage and field of application for the period 2010-2015. [ABSTRACT FROM AUTHOR]

Growing global supply chain trends across different industries have driven enormous changes and complexity of traditional supply chain management (SCM). Simultaneously, unprecedented disruptions impacting worldwide commerce further add more vulnerabilities to current SCM practices. The most recent Covid-19 pandemic is a major global catastrophe that has created unpredictable disruptions towards supply chain activities and caused interruptions, especially in supply or sourcing activities. This low-frequency and high-impact disruption have created severe ripple effects in SCM, resulting from propagation of disturbance from upstream point of supply to downstream part of production and distribution. While traditional SCM focuses more on developing initial strategies to avoid forecasted future risks, Covid-19 phenomenon has forced SCM practitioners to propose modern SCM with agile and resilient risk management capability to adapt and respond quickly to unpredictable risk. However, it is found that only a scarce number of academic papers have suggested appropriate supply chain risk management (SCRM) solutions for the commercial sector while most addressed the issues in humanitarian SCM. Hence, this paper aims to investigate the phenomenon of supply shortage as the root problem of ripple effects in the supply chain caused by Covid-19 pandemic and propose effective SCRM to build an agile and resilient commerce supply chain for responding to the disruptions. Beyond that, the objective of this research is to also provide a clearer picture of the critical role of SCRM in the future post-pandemic world that is advantageous for supporting agile and resilient decision-making processes to turn risks into opportunities. The study first provides a comprehensive review of existing literature related to SCM and SCRM as the basis to define measurements in assessing and improving supply chain agility and resilience. Based on this preliminary study, a simulation modeling approach based on real-life case study is designed, and experimental results are presented to depict more conceptual and realistic illustrations. This paper implements a combination of agent-based and discreteevent simulation methodology with AnyLogic simulation software. Through this proposed approach, the paper finally concludes with providing managerial insights on SCRM, particularly for strategic sourcing and identifying possibilities for potential research areas. The findings and recommendations from this paper can be further exploited for advancing SCRM based on simulation technology in real-life practices as an integral part of modern supply chain capabilities. In turn, this paper contributes to help supply chain practitioners accumulate better understanding on the importance role of agile and resilient SCRM, especially in sourcing activities during disruptions period, and effective methodology for its real-world implementation that supports making investment decisions in priority settings. [ABSTRACT FROM AUTHOR]

The paper presents a solution of the maintenance and repair organization (MRO) problem based on multi-agent technology. A generalized model of intelligent agent activity in road maintenance and repair techniques developed. An analysis of existing architectures agents has been executed. The hybrid InteRRaP agent architecture with internal simulation subsystem constituting a simulation engine described (it is a complex system dynamic models). The agent simulation apparatus implementation approaches based on the system-dynamic models in the multiagent environment proposed. The structure, basic subsystems and mechanisms of agents' interaction described. The stages of the continuous organizational process of production equipment maintenance and repair are shown. The proposed system allows obtaining more rational MRO. [ABSTRACT FROM AUTHOR]

Identifying individual expertise is critical to organizational learning, particularly when organizations grapple with complex problems. Yet identifying experts who are willing to help poses a challenge for many organizational members. Based on where it resides in organization, we categorize information into two types - local and global. Local information resides only within organizational members, whereas global information is retained within organizational repositories. These two categories of information pose a trade-off between the likelihood of knowledge transfer and the quality of the transferred solutions, which in turn, affect organizational performance. This paper investigates the effects of local versus global information on organization learning efficiency (the speed at which organizational members reach a consensus on solution) and effectiveness (the quality of the solution) conditional on various social structures. We find that in organizations with close-knit social networks, local information improves efficiency of solution transfer. By contrast, in organizations with tenuously connected networks, global information improves the efficiency of knowledge transfer. Coupling local and global information, regardless of the network's structure, most improves learning efficiency. Nevertheless, the presence of global information reduces the proactive experimentation in pursuit of novel solutions (organizational effectiveness). [ABSTRACT FROM AUTHOR]

The automated detection of aspects of spatial behaviour in an agent-based model is necessary for model testing and analysis. In this paper we compare four predictors of herding behaviour in a model of a grazing herbivore. We find that a) the mean number of neighbours adjusted to account for population variation and b) the mean Hamming distance between rows of the two-dimensional environment can be used to detect herding. Visual inspection of the model behaviour revealed that herding occurs when the herbivore mobility reaches a threshold level. Using this threshold we identify a limits for these predictors to use in the program code. These results apply only to one set of parameters and environment size; future research will involve a wider parameter space. [ABSTRACT FROM AUTHOR]

Agent-based Modeling is playing a key role in an increasing number of approaches addressing modeling complex systems. Historically, such models were focused on describing the system modelled dynamics but not the interaction or visualization of the model itself. The new requirements for high-level realistic visualization and online analysis tools of ABM simulations raise key issues that are yet unsolved: how to visualize interaction between entities and more generally abstracting the key information from the system dynamics. Instead of ad-hoc existing approaches that require reification in the model, we propose an approach dedicated to visualize and discover emerging dynamics from the model. In this approach, the execution of the simulation and its representation is separated and building a visualization is like building a visualization model on top of the reference model. The techniques presented here have been implemented in an Agent-based simulation platform, Gama, and illustrate the new perspective on visualization in Agent-based Modeling. [ABSTRACT FROM PUBLISHER]

This work describes the architecture of an agent based computer model that simulates the autoimmune response in Type 1 diabetes in a three-dimensional cubic volume representing a fragment of pancreas of a mouse. Immune cells and β-cells are modeled as agents, making the abstraction of one cell - one agent. We present our preliminary results for the interactions between β-cell agents and macrophage agents and a model of cell motility in the continuous three-dimensional volume. This agent-based approach allows implementing multi-scale computational modeling capturing subcellular characteristics into cell agents and obtaining outcomes from agent populations at tissue and systemic levels. [ABSTRACT FROM AUTHOR]

Taking a knowledge-based approach to innovation, we distinguish between four different innovation strategies that vary in how knowledge is sourced and exploited - Open Innovation (OI), Closed Innovation, Outbound OI, and Inbound OI. We build an agent-based simulation model to explore the competitive performance profiles of these strategies as well as their innovation creation potentials. Our simulation research allows for modeling the micro-foundations of open innovation and for studying innovation strategies in different market conditions. We find that the relative financial payoffs associated with the different innovation strategies vary with time - some strategies win out in the short-term others in the long-term. The results also suggest that there is a trade-off between a strategic orientation based on innovativeness versus one based solely on financial returns. [ABSTRACT FROM AUTHOR]

The purpose of this exploratory study is threefold: first, to bring reference groups back into spiral of silence (SOS) by proposing an extended framework of dual climate of opinion; and second, within the framework, to analytically explore the boundary conditions of the stable existence of SOS; third, to analyze the dynamic characteristics of the process in the aspects of spatial variation and temporal evolution. By agent-based modeling of SOS, the findings suggest there is no guarantee of SOS after reference groups being brought back, and stable existence of SOS is contingent upon the comparative strength of mass media over reference groups. Additionally, SOS is size-dependent upon reference groups and the population. Furthermore, the growth rate of SOS decreases over time. This research extends classic theory of SOS to include reference groups, which sheds light in understanding the interplay among individuals, reference groups, and mass media. [ABSTRACT FROM AUTHOR]

Tiebout's model operationalizes assumptions about decentralized behavior of a population of autonomous individuals who seek greater utility through choices between jurisdictions with different political institutions designed to aggregate preferences. Based on an agent-based model inspired by Tiebout's model, we discuss the validity of these assumptions for the case of empirical situations present in capitalist societies. [ABSTRACT FROM PUBLISHER]

Problems related to brown plant hopper outbreaks management in the Mekong Delta-Vietnam are complex and need aggregating information across administrative scales. Brown plant hopper density and environmental indicators are main factors that support stakeholders have suitable decision making. Due to detail levels of information are different from field scale to region scale, upscaling information is required, this paper aims at designing an agent-based assessment model and proposes an upscaling method to aggregate and assess information from field to region scales. A case study presents conveying rice area infested by brown plant hoppers across scales and results could help stakeholders assess damage levels caused by Brown plant hoppers in rice crop seasons. [ABSTRACT FROM PUBLISHER]

Studies in predicting rice pest outbreaks use regression models to explore relationships between rice pest population density and environmental factors. Agent-based models provide a flexible assessment framework to help stakeholders in planning rice pest management strategies. This paper proposes a general method to determine rice pest risk index and design a structure of agents that have an ability of aggregating information from other agents in agent-based models, OWA-based method is experimented on multi-criteria decision making agents to determine rice pest risk index of each rice area. A case study is carried out on risk assessment of brown plant hopper in the Mekong Delta region, simulation results in this study show that rice pest risk index aggregated from criteria and agent-based approach are useful tools to support stakeholders in rice pest management. [ABSTRACT FROM PUBLISHER]

This paper proposes an orchestration model for post-disaster response that is aimed at automating the coordination of scarce resources that minimizes the loss of human lives. In our setting, different teams are treated as agents and their activities are “orchestrated” to optimize rescue performance. Results from simulation are analysed to evaluate the performance of the optimization model. [ABSTRACT FROM PUBLISHER]

One of the greatest benefits offered by cyber-physical systems is the potential for automated decision support, which can increase the intelligence and efficacy of environmental management. Agent-based modeling can facilitate the application of cyber infrastructure to environmental decision support, by abstracting physically-distributed and communication and control into the operation of one or more agents. Ontologies can capture the semantics of the operation of both physical and cyber components of an environmental management system, respectively, while reflecting interactions between the two. As such, they can serve as a basis for automated reasoning by agents for intelligent decision support. In this paper, we illustrate and validate the use of ontologies in decision support for an intelligent water distribution network. The focus of the decision support is on identification and mitigation of failure, the aim is dependable distribution of potable water. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a general framework for evaluating the transitions toward sustainable alternative fuel vehicles, which takes into account supply and demand of both vehicles and fuels in the market. The modeling approach and underlying mathematical formulations for different stakeholders are described and are then integrated in one model of the whole energy and transport system. The hybrid modeling framework integrates the top-down system-dynamics approach with the bottom-up agent-based modeling. We specify the integration process and the most important linking variables between various energy and transport components. Finally, the potential contribution of hybrid modeling approach to the models of transition to sustainable mobility will be concluded. [ABSTRACT FROM PUBLISHER]

Organizations today compete in a knowledge-based economy. In the Foresight 2020 survey, knowledge management (KM) is believed to offer the greatest potential for productivity gains over the next decade. However, in practice many organizations do not receive satisfactory value from implementing KM. The primary reason of low return on KM investment is due to reuse problems. We propose an agent-based model to assessing knowledge reuse processes in an organization. By modeling major components in knowledge reuse processes as agents, we provide a way to analyze the interactions and relationships among these individuals, and their impact on the value in organizational knowledge reuse. This paper presents a preliminary model of our work-in-progress. [ABSTRACT FROM PUBLISHER]

The article presents abstracts on organization and management theory topics which include differences between Convention Theory and New Organizational Institutionalism, the relationship between status and uncertainty, and the people variations in networks that can affect organizational performance.

Studies have shown that issue attention in the mass media is not steady and does not necessar-ily correspond to objective real world criteria. Furthermore, it often follows inherently dy-namic waves of ups and downs, which are highly nonlinear and in most cases unpredictable. Existing frameworks for studying media attention dynamics comprise models of issue atten-tion cycles like proposed by Luhmann and Downs in the 1970s combined with theories of news selection and agenda-building. These approaches succeed in retracing issue careers and isolating numerous factors, which could explain their characteristics. However, the interplay of these factors and the mechanisms, which could produce the observed discourse dynamics have not yet been sufficiently analyzed. The paper proposes to advance the existing ap-proaches by embedding them into the interdisciplinary perspective of complexity theory. Concepts like nonlinearity, emergence, self-organization and feedback are introduced, and their use for studying dynamics of mass mediated risk discourses is made clear. Additionally, the paper also shows that methods of the complexity perspective, respectively agent-based modeling and simulation, can be meaningfully applied to analyze the underlying mechanisms of issue attention. It concludes by proposing a basic design for an agent-based model of media attention dynamics. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

In many social contexts, individuals choose a set of social interactions to maximize their private benefit, but the resulting social network structure is a public phenomenon that affects all members of the network. This paper builds on previous research by the authors (Lazer and Friedman 2005), which found that under certain circumstances efficient collaborative networks (e.g. small world networks) yield poor system-wide results. In this paper, we assume that the structure and efficiency of the network is endogenous, the result of an evolutionary process where "networking strategies" that yield greater success for agents will eventually dominate the system. Results of an agent-based model show that while sparse networks perform better in the long run because they maintain diversity, individual actors that are well connected systematically do better than poorly connected actors. The long run equilibrium is a highly connected network, through which information flows rapidly, wiping out diversity, and resulting in a poorly performing system. The "tragedy of the network" is that if everyone acts in their best interest, the resulting network will be worse for everyone. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

Since Granovetter's frequently-cited work on the role of social networks in the labor market, many have tried to document the relationship between networks and labor market outcomes. However, the results of these studies are quite inconsistent. In this paper I specify two distinct theoretical mechanisms through which networks could affect labor market outcomes, and outline how each interacts with important structural features of networks and other labor market practices. Results from a computational experiment using these mechanisms accounts for many of these formerly "inconsistent" findings. Specifically, results confirm that context, in the sense of global network structure, strength of employment relationship, and employers' preferences, has profound consequences for careers, the distribution of opportunity through the population, and the meaning of "good" and "bad" networks. These theoretical advances are particularly important in light of evidence that there has been a general decline of "career jobs," a trend that puts renewed pressure on the job-matching process and those extra-organizational institutions--such as social networks--that play a role in job-seeking and hiring. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

Empirical evidence shows that many workers search for jobs through their networks, and that employers often prefer to hire through referrals. As a result, many have expected that who use their social networks will enjoy a labor market advantage over those who do not. However, as Mouw has documented, this pattern is not consistently observed empirically (Mouw 2003). In this paper we argue that networks might play a role in producing or buffering against various types of occupational instability (career turbulence) through the mechanisms of information and influence. Our results suggest that the perplexing lack of empirical support for the social network advantage is explained in part by differences in the local and global structure of networks and by variation in the strategies and preferences of employers. Using an agent-based simulation, we show that broader networks reduce the frequency of turbulent events in careers, and that dense networks can trap workers into unstable cycles of job holding. However, these results can be both amplified and attenuated by characteristics of global network structure of the network, and by employers' preferences for hiring through referral. We discuss the implications of these findings and make specific suggestions for important areas of future empirical research. [ABSTRACT FROM AUTHOR]

The article discusses a model of race discrimination that is based on wealth inequality. A research revealed that African Americans are unable to decrease the income gap due to persistence of racial discrimination. The model reveals that an initial wealth differential can result into disparity between racial groups. The reason is the interaction among agents with a large wealth differential teaches members of the wealthier group to dislike the opposing group. However, without a wealth differential even groups who exhibit animosity toward other groups will learn the ways to interact.

The article presents abstracts of conference papers related to technology and innovation management. "Deploying Intellectual Capital for Architectural Innovative Capabilities," by Mohan Subramaniam; "Exploring Industry Implications of Organizational Learning," by David R. King; "The Fragility of Knowledge Sharing: A Behavioral Approach," by Georg Von Krogh; "When Do Acquisitions Facilitate Technological Exploration and Exploitation?," by Paul Almeida.

This section presents abstracts of conference papers about managerial and organizational decision making. Some of the abstracts include "Emotional Intelligence and Communication Perceptions During Conflict and Decision Making," focusing on how business intelligence helps organizational effectiveness, "Knowledge Sharing and Team Effectiveness in Challenging Organizational Environments," about how management teams benefit from external knowledge, and "A Small World After All? Network Perceptions Versus Reality," that discusses how social networks can affect an organization.

We examine the evolution of cooperation in social dilemmas with intergroup mobility. For this purpose, we use a computational method with an agent-based model. We run social dilemma simulations, in which agents can freely move to other groups. In such situations, tit-for-tat needs to escape because free-riders can invade cooperative networks. In the simulation, four representative strategies are compared: two cooperative strategies (exiting and fixed tit-for-tat strategies) and two noncooperative ones (exiting and fixed defectors). All possible initial distributions are tested (1771 combinations), each with 30 simulations. From them, two findings are obtained. (i) Cooperators can exclude free-riders if there are enough initial cooperative agents. This is because they can form cooperative networks. (ii) Moreover, when initial defectors increase, matching matters. We observe a cyclic relationship on the robustness of the four strategies. [ABSTRACT FROM AUTHOR]

A boss-less organization, where employees self-select to projects, have been an emerging phenomenon that has attracted attention of both scholars and practitioners. The proponents of this new form argue that it offers considerable advantages over the more traditional hierarchical approach. The empirical evidence, however, offers mixed results. This paper develops a simple agent- based model of open and hierarchical allocation to determine the mechanisms and boundary conditions of application of the two opposing modes of project selection and asset allocation. Our results suggest that the relative balance between the resources of the organization and number of opportunities it faces play a critical role in determining the efficacy of the two forms. We provide a detailed analysis of the mechanism and discuss potential applications of our findings to studies of industry evolution and firm size. [ABSTRACT FROM AUTHOR]

We develop a location analysis spatial model of firms' competition in multicharacteristics space, where consumers' opinions about the firms' products are distributed on multilayered networks. Firms do not compete on price but only on location upon the products' multi-characteristics space, and they aim to attract the maximum number of consumers. Boundedly rational consumers have distinct ideal points/tastes over the possible available firm locations but, crucially, they are affected by the opinions of their neighbors. Our central argument is that the consolidation of a dense underlying consumers' opinion network is the key for the firm to enlarge its market-share. Proposing a dynamic agent-based analysis on firms' location choice we characterize multi-dimensional product differentiation competition as adaptive learning by firms' managers and we argue that such a complex systems approach advances the analysis in alternative ways, beyond game-theoretic calculations. [ABSTRACT FROM AUTHOR]

How do organizations form project teams to respond to an ongoing stream of different projects? This study considers team formation as a phenomenon shaped by organization structure and learning from project experience. We address the effects of two alternative organization structures (functional and team-based), project stream dynamism, and a set of project attributes (size, ambiguity, and decomposability) on the efficiency and reliability of project team staffing. We build these features into an agent-based model grounded on multiple cases studies of project-based organizations. This model highlights transactive memory development as a key mechanism to improve the team formation process over time. Transactive memory guides the search for individuals with diverse skills who collectively are capable of solving clients' problems. Our analyses show how different organization structures, levels of project stream dynamism, and project attributes shape the development and application of transactive memory, thereby affecting the efficiency and reliability of the team formation process over time. [ABSTRACT FROM AUTHOR]

In many social contexts, individuals choose a set of social interactions to maximize their private benefit, but the resulting social network structure is a public phenomenon that affects all members of the network. This paper builds on previous research (Lazer and Friedman 2007), which found that under certain circumstances efficient collaborative networks (e.g., small world networks) yield poor system-wide results. In this paper, we assume that the structure and efficiency of the network is endogenous, the result of an evolutionary process where "networking strategies" that yield greater success for agents will eventually dominate the system. Results of an agent-based model show that while sparse networks perform better in the long run because they maintain diversity, individual actors that are well connected systematically do better than poorly connected actors. The long run equilibrium is a highly connected network, through which information flows rapidly, wiping out diversity, and resulting in a poorly performing system. The tragedy of the network is that if everyone acts in their best interest, the resulting network will be worse for everyone. [ABSTRACT FROM AUTHOR]

Putting a price on carbon emissions has, in recent years, emerged as the single most promising component of any combination of strategies necessary to mitigate climate change, and is an attempt at correcting externality-omissions in cost calculations within capitalist economic frameworks. Our study examines some design alternatives for such a pricing system by exploring the fledgling Alberta carbon market. We attempt to evaluate the performance of these designs on the bases of trade volume, cost efficiency and stability. To achieve this we construct an empirically-calibrated but simple agent-based model, certain aspects of which we selectively modify to incorporate various design options. We make comparisons among these options based on data simulated from the ensuing family of models. We find strong evidence that market design features such as source-of-credits, the scale of the market, and pricing-mechanism are very important considerations that influence the performance of the market. In addition, we find support for the notion that the level of the price cap relative to the average cost of abatement in the market matters, and beyond a threshold, higher price caps are associated with lower levels of performance. Our study holds insights for academic researchers interested in climate issues and market regulation, and presents an analytical tool for stakeholders in government and regulated corporations. [ABSTRACT FROM AUTHOR]

Innovation is largely a product of teams. By sharing ideas with other group members, teams can leverage good ideas for innovative outputs to a greater degree than creating alone. However, group processes can stymie or ignite the creative process by influencing idea sharing among group members. Thus, we propose investigating how sociopolitical group dynamics shape the likelihood of team members contributing new ideas and supporting the ideas of others. We develop an agent-based model to simulate how both team demographics and network attributes influence creative outcomes of teams. Our simulations suggest demographics and network attributes play a role at different stages of the idea sharing process. [ABSTRACT FROM AUTHOR]