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Agent-based analysis and simulation of the consumer airline market share for Frontier Airlines
Abstract
The complex and interconnected world in which organizations operate presents many challenges to the traditional neo-classical view of research and management and associated research techniques. Fundamental to the operation of financial capital markets, investor confidence relies on accurate investment analyst earnings forecasts. We propose agent-based modeling (ABM) as a viable tool to account for the interaction of local and environmental factors to determine organizational success. In an illustrative case study of Frontier Airlines, we develop and execute an ABM of Frontier’s consumer airline market to derive market share for the upcoming year. In the model, Frontier is impacted by internal policies, competitors, and environmental factors of fuel costs, federal regulation, and credit availability. We conclude with a discussion on how ABM can be effectively incorporated into future research activities and decision-making situations.
... Deployment of a runtime system is discussed and how measurements at runtime can be used to reveal unknown Knowledge Nodes and interaction pattern inside a DKMS (Schwotzer 2004) Faucher et al. (2008 argues that Existing models are logically extended, by adopting a complexity-based perspective, to propose a new model which highlights the non-linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of metas (such as meta-data, meta-information, and meta-knowledge) is discussed, and a reconstitution of knowledge management is proposed (Faucher et al. 2008) Kuhn et al. (2010) propose agent-based modeling (ABM) as a viable tool to account for the interaction of local and environmental factors to determine organizational success. They develop and execute an ABM of Frontier's consumer airline market to derive market share for the upcoming year. ...
... In the model, Frontier is impacted by internal policies, competitors, and environmental factors of fuel costs, federal regulation, and credit availability. They conclude with a discussion on how ABM can be effectively incorporated into future research activities and decision-making situations (Kuhn et al. 2010) VI. KNOWLEDGE EMERGENCE One feature that reinforces the appropriateness of the application of complexity theory to knowledge management is the emerging nature that various authors acknowledge it. ...
In this article, a Literature Review concerning a new perspective of Knowledge Management (KM) framed in Complexity Theory in general, and Complex Adaptive Systems (CAS) in particular, is presented. As background, some of the criticisms of the mainstream of Knowledge Management, and the most relevant features of Complex Adaptive Systems, are presented. Then one by one the categories with which we have found in the literature intersections with the main categories (KM and CAS) are described. Finally the conclusions of the review are presented.
... They simulated the interactions between air traffic control system command centres, air route traffic control centres, airline operation centres, and flights (Mehta et al., 2006). Another notable agent-based model is developed by Kuhn et al. (2010) to model the frontier airline market share. They used past purchasing behaviour and perception of airline performance as the main decision rules of passengers. ...
... They used past purchasing behaviour and perception of airline performance as the main decision rules of passengers. They found that the output of the model was very close to the real market share of the airline which was an external evidence to validate the model (Kuhn et al., 2010). ...
In network industries such as the airline industry competitions and collaborations between organisations shape the dynamics of the market significantly. The conditions under which firms choose to collaborate instead of competing are of particular importance in understanding the effects of regulatory actions within such industries. In this paper, an agent-based simulation and modelling approach is used to study the dynamics of competition and collaboration among airlines in the USA under different regulatory conditions and corporate strategic choices. The analysis is limited to a single competitive domestic flight corridor (New York City to Los Angeles). The results of the set of developed models show that both individual corporate strategies and government policies can have a significant impact on competition and collaboration dynamics of the system.
... Such changes threaten to impair company competitiveness if unnoticed. Some scholars later emphasized that knowledge structure complexity (amount of information or the number of elements) should elevate organizational members' ability to respond to environmental changes and new situations [14,15]. A more complex knowledge structure can encompass a greater number of new situations and problems and can help a firm overcome higher levels of uncertainty, which then encourages cooperation across departments, such as in the case of NPD teams, where more diverse information can be able to recognized and processed. ...
... Marketing members can combine marketing analysis, product specification and patent context and then offer an integrated marketing plan to the consumer. 15. The NPD team applies information learned from new product post-launch meetings and knowledge accumulated from the prototype to match clients' needs. ...
When a new product development team faces challenges, such as the cross-functional knowledge conversion task, both simple and existing organizational structures are comprised of various management methods and knowledge characteristics, analogous to a fully armed military force. However, these features are not arranged in order of priority. Each step within the knowledge conversion process of new product development may not require such a full depot of management methods and keynotes. Therefore, this study adopted Blackler’s perspective to examine the suitable organizational knowledge structure for cross-functional knowledge conversion within new product development teams.
... 21 Ibidem. 22 Ibidem. 23 Proceedings of the 26th International Conference of the System Dynamics Society July 20 -24, Athens 2008. ...
W artykule przedstawiono możliwość zastosowania symulacji komputerowej w badaniach marketingowych, ze szczególnym uwzględnieniem dwóch jej metod - dynamiki systemowej i symulacji wieloagentowej. Omówiono istotę tych metod oraz zaprezentowano główne kierunki zastosowań w dziedzinie badań marketingowych.
... Although few studies like Ref. 79 have used system dynamics to investigate the consumer behavior, multiplicity of variables and the interactions between them still challenge the modeler in using this method. Moreover, consumers' preference studies in the¯elds of psychology, economics, sociology and marketing include constituents complex social systems (see Refs. 12,30,34,42,58). Despite ABM approach being a strong alternative for consumer behavior modeling as proven by studies of Refs. ...
Despite extensive studies on consumer behavior and decision making, the social influence of consumers on each other has not been widely investigated. To incorporate such interactions, in this study, we propose and apply an agent-based simulation model where consumers are defined as agents. The purchase behavior of each agent is characterized as a function based on the concept of the black-box model for consumer behavior. In particular, we investigate the effect of consumers’ social network and its interaction with the marketing mix parameters (4Ps). A case study of household appliances in a local market is used to demonstrate how the dynamics of preferences between domestic and foreign brands occurs. The simulation model is used to examine the effect of eight scenarios related to these interactions. The obtained results are compared and the most important factors are determined as product features and price.
... Another important area of application of the agent-based approach concerns the study of purchasing trends in specific markets by simulating many individual consumers' choices to determine how and why consumers choose a given product or service. Applications of this type are discussed in Twomey and Cadman (2002), Robertson (2003), Schenk et al. (2007), Ulbinaitė and Moullec (2010), Kuhn et al. (2010) and Fikar et al. (2019). ...
... If we have more guiders, signs, and Exits in our cities, we certainly can reduce the death caused by possible attacks and related stampedes. Agent-based modeling and simulations have become the useful and emergent methods for interdisciplinary researches, such as crowd dynamic and evacuations [2,4,19,28,31,34,48,50], airline market [81], collective actions and cooperation [3,5,82], disasters [3,41,59,78], as well as terrorist attacks & stampedes [1,4,22,54,68,83]. We will continue to promote the agent-based modeling of terrorist attacks. ...
As a global problem, the terrorism leads to high death tolls each year. During terrorist attacks, the direct death is caused by terrorists attacking civilians. However, indirect death caused by stampedes should not be underestimated. Under great panic, most civilians were running disorderly, rushing into limited number of Exits, which causes stampede injuries and deaths. To explore this dual-mechanism dynamics, we build the agent-based modeling of particle system. Civilians lose blood when attacked, which is the attack mechanism, or crashed and trampled by others civilians, which is the stampede mechanism. For all civilians, the blood variable determines the physical status of being strong, healthy, weak, injured, and dead. Five key factors, such as the perception range, the number of Exits, the density of civilians, the number of terrorists, and attack strategies, are introduced into the model. We run each simulation repeatedly for multiple times and take the averaged survival rate, attack death, and stampede death as robust outcomes. The collision damage has the phase transition effects between stampede and attack deaths. The perception range R have the peak effect on the survival rate and the trough effect on both stampede and attack deaths. It expands understandings of human behavior dynamics, and helps to predict the dynamics and outcomes in advance. The optimal perception range can be solved accordingly, to practically guide the public facility planning and regular emergency training in real-life.
... Roozmand et al. [14] put forward an agent-based model for the analysis of consumer decision-making based on culture, personality and human needs in eleven European countries. Kuhn et al. [15] summarized advantages of the ABMS in the field of consumer behaviors, and they pointed out that this methodology had a large development space in consumer behaviors area. ...
... Many individual consumer choices are simulated to define how and why consumers choose a certain product or service. This type of approach is described by (Collings et al. 2000;Brannon et al. 2000; (Twomey and Cadman 2002;Wohltorf and Albayrak 2003;(Robertson 2003); (Kyrylov and Bonanni 2004;Schenk et al. 2007;Ulbinaite and Le Moullec 2010;Kuhn et al. 2010). ...
The consumer is a key element in the marketing. Examining consumer behavior allows for a better understanding and forecasting factors influencing purchasing decisions, which in turn facilitates the formulation of effective marketing strategies. The aim of this study was to work out methodological basis for analyzing consumer behavior with application of agent-based simulation (ABS) as well as conducting simulation experiments using the elaborated methodology. The chapter presents ABS in the context of its applications in marketing research, proposes the methodology for consumer behavior analysis with the use of ABS, describes the concept of agent-based simulation model for investigating consumer behavior, and finally shows the results of simulation experiments executed for a case study. The case study focuses on the consumer behavior to buy electric appliances in Basra city and experiments relates to five exemplary marketing strategies. The results of the experiments confirm that the agent-based model can be a powerful tool for examining alternative marketing strategies rapidly, relatively cheaply, without requiring the actual commitment of resources.
... Agent-based modelling (ABM) is a computational simulation methodology (Kuhn et al., 2010) used in social science, biology, and other fields, which involves simulating the behaviour and interaction of many autonomous entities, or agents, over time (Chertow andEhrenfeld, 2012, Ghali et al., 2017). Agent-based models, allow bottom-up (Fraccascia et al., 2017) simulations of organisations constituted by a large number of interacting parts. ...
Even though eco-industrial parks (EIP) models have proved to transform industrial areas by strengthen the emergence of sustainable EIP, there is a noticeable lack of research addressing the economic returns of the participating companies in the network which fluctuates according to prices offered for the resource exchange over time. In this paper, we develop an agent-based model sometimes refer to as bottom-up approach for the design of EIP in which price fluctuation and demand variability are emergent properties of the interaction among the agents. Agent-based modelling (ABM) is a computational methodology used in social science, biology, and other fields. It represents autonomous entities, each with dynamic behaviour. The agents within the eco-industrial park are the factories, market buyers and market sellers. The computational development was performed in Réseau.py, which was built in Python (a programmable modelling environment) from scratch. Based on the autonomy of each of the agents and their individual objectives, simulations were carried out on a bio-energy based EIP (BBEIP) system in order to study the influence of price fluctuation between the agents. The results show that variability in price is a factor for establishing symbiotic relationship among the symbiotic agents in the EIP.
... We propose to model this political scenario using an agent-based model (ABM) methodology [7,23,45]. ABM has been broadly applied for social simulation [27,37,46,61] and for modeling political scenarios [40,43,51]. ...
... We propose to model this political scenario using an agent-based model (ABM) methodology [7,23,45]. ABM has been broadly applied for social simulation [27,37,46,61] and for modeling political scenarios [40,43,51]. ...
... have investigated the dynamic behaviour of an AOC centre of a major airline using a discrete event model. Kohl et al. 2007 have studied numerous aspects of airline disruption management, and argue that realistic approaches to disruption management must involve humans in the key parts of the process. Feigh 2008 has examined the work of airline controllers at four US airlines of varying sizes, and applied an ethnographic approach for the development of representative work models. ...
Purpose: In order to improve the safety, capacity, economy, and sustainability of air transportation, revolutionary changes are required. Implementing such changes can introduce both negative and positive emergent behavior. Currently, the inability to understand and control such behavior prevents us from avoiding undesired negative emergent behaviors and promoting positive ones. In order to address this problem, this thesis aims to understand emergent behavior in the complex socio-technical air transportation system.
Methods: The thesis proposes Agent-Based Modelling and Simulation (ABMS) as a method for capturing emergent behavior of the socio-technical air transportation system, and evaluating novel system designs. The thesis focuses on two main applications namely: 1) the identification of emergent safety risk of an active runway crossing operation; and 2) the evaluation of the role of coordination in Airline Operations Control (AOC) resilience. The agent-based models include all relevant human and technical agents, such as pilots and controllers and the decision support systems involved. Simulation of these agents interacting together is conducted to predict the impact of both existing and future concepts of operation operations
Results: The applications in this thesis highlight that ABMS has the capability to reveal unexpected emergent behavior and provide novel insights in air transportation. For the airport safety application, various types of emergent behavior have been revealed due to the development and simulation of the agent-based model. For the airline resilience application, novel insights were gained about the role of coordination in airline resilience.
Conclusion: This thesis demonstrates that ABMS of air transport operations is a viable approach in gaining knowledge about emergent behavior which was unknown before. This knowledge includes both bottlenecks of system designs and identified opportunities, and hence can be used to control and further optimize the socio-technical air transportation system. This also implies that ABMS can be a cost-effective method for evaluating new concepts during the early design phase of air transport.
... Airport Marketing Models: Existing airport marketing models indicate that [14] presented an ABM to assist in market share analysis that helps the investment analyst develop earnings forecast for the year ahead. It is clear that there are limited existing models in this area, and the available one used ABM to model a macro level factor, which could best be studied using an aggregate viewing tools for better prediction of model behaviour. ...
Airports are complex socio-technical systems with many different stakeholders which often have very different needs. Operations Research modelling tools and techniques are used successfully to support the management of such systems by helping to better understand and improve their operations. A review of the literature shows that there are many existing modelling studies related to airport operations management but it also unveils some territories that are still untapped. This might be due to the fact that the right tools or techniques for these jobs have not been identified yet. In this paper we identify some of these untapped areas and discuss how simulation modelling could be used as a decision support tool for gaining insight into systems operation in these areas. We take a closer look at one of the identified areas (airport facilities maintenance scheduling) and conduct a hypothetical proof-of-principle simulation study to demonstrate the feasibility and applicability of using simulation in this area. Our conclusion is that simulation studies are a very useful aid for this kind of investigation.
... The study concluded that ticket subsidies combined with measures to enhance non-aeronautical revenue are needed for viability of these regional airports. Another study loosely related to the topic is that of Kuhn et al. (2010), who present an AB model to assist in market share analysis. The model is supposed to help investment analysts to develop earnings forecast for the year ahead. ...
Marketing airline products and services has always been highly competitive and requires that rigorous strategic planning is put in place for achieving maximum growth and profitability. Customer relationship management which is one of the factors that has direct impact on the overall performance of an airline must be guided and maintained by changing organisational internal and external marketing plans. However, it is very dangerous to find solutions to problems that involve customers and strategic planning by experimenting with real subjects. Therefore, simulation studies have become one of the ways of proffering solutions to such problems. In this paper we present a hypothetical proof-of-principle study that was conducted to demonstrate the feasibility and applicability of using System Dynamics (SD) simulation for studying airline marketing strategies. In conclusion we can say that SD simulation has shown strong potential as a decision support tool in this instance, and we are confident that our prototype can be used as a basis for investigating real-world cases. © ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors).
... They analyzed the improvements in air traffic management using a new collaborative structure for NAS. Lim et al. proposed a general framework for modeling airline industry [28], and Kuhn Jr. et al. used ABM for estimating Frontier Airline market share [29]. Moreover, Giap Lim did a study for understanding the relationship between demand and supply in the air transportation network to test different scenarios of fuel costs [30]. ...
Network industries include a multitude of organizations and companies delivering valuable services and products for modern life. Surprisingly, the impact of the structural complexity of their underlying physical infrastructure on the behavior of these organizations has not been explored yet. The aim of this paper is to study the role of an initial network structure on the throughput of these complex systems, and to provide evidence for the value of using agent-based modeling (ABM) in governing competition and collaboration in network industries. A two-stage multiround game provides the mathematical foundation to examine the behavior of players in the complex interconnected structure of a network industry. The United States Air Transportation Network is used as the case study. The real data on the different elements of the system are embedded into an agent-based model to provide a descriptive model of behaviors within it. The outcome of the model shows path dependence in the system and highlights the impact of initial network conditions on the market. Moreover, this model provides evidence on the usefulness of ABM for understanding the interrelationships between economic behavior and the physical structure of the system.
... Agent-based simulation is a modeling paradigm that abstracts and models a complex system comprised of interacting autonomous agents (Kuhn Jr et al., 2010). It also relates to the term "agent-based modeling", "agent-based modeling and simulation", or "multi-agent simulation". ...
The overall goal of this Ph.D. research is to provide reference models, support me- thods and tools that simulate change propagations in a Product Development (PD) project to assist decision-makings. We firstly establish a change analysis framework of modeling the context of change occurrence and propagation by taking into account the multiple knowledge areas of PD project simultaneously. Under the framework, we propose the conceptual models of change occurrence and change propagation that pro- vide a qualitative method to identify change and change propagation and imply some characteristics of change propagations. Relying on that, we suggest the procedures of building up the change propagation networks. Within the network, we propose the methodology of simulating change propagations and then present the process of im- plementing the methodologies and the models as a software prototype by using multi- agent based technology.
... Although agent-based simulation models provide an important step towards capturing the nonlinear and stochastic processes of individual agents and environmental factors, and seek to embrace rather than "control away" individual factors, the models are wholly dependent on the original inputted information. The cautionary adage, "garbage in, garbage out" is particularly pertinent when considering the usefulness of agent-based simulation models (Kuhn Jr, Courtney, Morris, & Tatara, 2010). The inputs in agent-based models must be kept relatively simple, as highly complex models featuring multiple agents and interactions will yield models that are extremely difficult to interpret and validate (Auchincloss & Diez Roux, 2008). ...
Purpose: Risk assessment has become an important tool of forensic social work. As incarceration may increase a juvenile’s exposure to criminogenic influences, and lead to a greater disconnect from pro-social supports, the implementation of validated risk assessment tools may increase the Parole Board’s confidence to release more juveniles early on parole, rather than relying on habits, traditions, and guesswork. One such risk assessment instrument, The Structured Assessment of Violence Risk in Youth(SAVRY), was adopted by New Jersey’s State Parole Board in 2009. The SAVRY instrument guides assessors through a list of 24 risk factors grouped under the domains of historical risk, social/ contextual factors, individual/ clinical factors, and six protective factors. Completion of the instrument leads to the generation of an overall “risk summary,” where the assessor concludes that the juvenile poses a low, moderate or high risk of future serious violent behavior.
Despite the potential utility of risk assessment tools, empirical research has not yet examined the actual impact of risk assessment instruments upon practice. Given the limitation in literature, this quasi-experimental study evaluates the outcome of this statewide risk assessment implementation, and hypothesizes that juveniles who were assessed by SAVRY would be more likely to receive parole than those who were not.
Methods
Study data were drawn from 445 parole case files, of which 236 juveniles had received a SAVRY risk assessment, and 209 had received no assessment. The two groups were drawn from all juveniles who were evaluated for parole between 2009 and 2011. The groups were matched on age, gender, ethnicity, sentence length, seriousness of offenses, and risk of reconviction.
A survival analysis using cox proportional hazard modeling was employed to assess whether SAVRY-assessed juveniles were likely to be released earlier than non-assessed juveniles, controlling for juveniles’ offense seriousness, risk of reconviction, age at release, and race/ethnicity being covariates.
Results
The majority (93.7%) of juveniles were male. On average, they were released at age 18.2 years. African Americans comprised 67.6% of the sample, followed by Hispanics (20.2%), and Caucasians (11.7%). The average sentence received was 22.9 months, with 42% of juveniles committing a violent offense, 19.8% a drug-related offense, and 15.5% an offense of robbery without physical contact with the victim.
Survival analysis results show that juveniles assessed by SAVRY were 1.71 times (95% CI, 1.300, 2.258; p< .0001) more likely to receive parole than those who were not assessed. Age at release was a significant predictor, as was the risk of reconviction, and offense seriousness score. The ethnicity of the juvenile was not a significant predictor of parole release.
Implications: These findings support the use of risk assessment instruments in juvenile justice decision-making. Juveniles who received a risk assessment are more likely to be released early on parole than those who did not. The SAVRY instrument appears to guide Parole Board decision-makers along a carefully constructed path of risk factors, encouraging probabilistic rather than possibilistic thinking.
... A more recent study by agent-based simulation is done by Kuhn Jr. et. al. to forecast the Frontier airline market share. They simulated consumer behavior, and airlines performance to predict the future of the Frontier's demand. They prediction was very close to the real outcome of the airline (Kuhn-Jr.,Courtney et al. 2010). ...
Public transportation is an important instance of network industry. The network externality, which is the increase of a product or service utility with increase in its network, is a challenge in regulating these industries. The proposition of this paper is to analyze the transportation system in two steps: (1) studying the system without externality, and (2) adding the effect of externality. To perform the first step, we propose to analyze the transportation system in one route and eliminating the network externality. Agent-based simulation is used in this study to model the dynamics of air transportation network, because of its button-up capability for analyzing system level dynamics. The model should be extended to cover the whole air transpiration network in United States in the second step. Since this model captures the complexity in system level, the complete model is useful for policy analysis and decision making for governance of this network industry.
... Meanwhile, logical agents have been studied for decades and implemented in many different research fields. In [28], authors present an Agent-Based Modeling (ABM) as a viable tool to account for the interaction of local and environmental factors to determine organizational success. A comprehensive knowledge base is constructed to help decision-making. ...
In the services computing environment, collaborations are constrained by different requirements from different service providers and consumers. Administrators in different providers and users from different consumers use policies to define control rules and configurations of application environments. These control rules and application configurations reflect different performance requirements, management needs, and business contracts. When collaboration is necessary between services for a specific task, various performance and management requirements from individual services and their providers may have conflicts. The situation is even worse when the collaboration is a one-time event. In policy-based systems, these conflicts are reflected in policy conflicts. Thus, we propose a knowledge-augmented logical analysis framework for these policy conflicts in order to make services collaboration possible and smooth. In our policy conflict analysis engine, a knowledge base is used to supply critical information for analyzing dynamic relationships, hidden information, and constraints on attributes and relationships. More importantly, this information is embedded in logic expressions and reasoning processes so that explicit and implicit constraints between different elements can be integrated into one logical analysis framework. Two different case studies in web services and sensor network environments and their corresponding experiment results confirm the strength and applicability of our proposed policy conflict analysis framework.
... To setup an agent-based framework, Kuhn et al. [98] suggest the following steps; The first three items have been discussed in the previous two chapters. The problem frame is the environmental impact at the system-of-systems level and how it is influenced by the introduction of novel technologies. ...
Anthropogenic environmental impact is growing despite large technical efforts to reduce it. Its dependency on technology induced human behaviour makes designing for environmental impact reduction particularly difficult. Despite the fact that the sustainable conundrum is characterized as a “no technical solution problem”, a framework is proposed in support of environmental impact reducing technology design. The goal is to provide a means to allow a priori evaluations of product design in a true stakeholder setting. The basis of the framework is formed by Quality Function Deployment (QFD), Value Engineering (VE) and Multidisciplinary Design Optimization (MDO), which allow for addressing the complexities at system and system-of-systems level by dedicated and tailored tools. This set of tools consists of Agent Based Modelling and Simulation (ABMS), Bi-Level Integrated Systems Synthesis (BLISS) and Bayesian Statistics. The design for sustainability support provided by each of these tools is illustrated by a different realistic show case. Since aviation possesses all complexities of the sustainable conundrum, it is used as a challenging example to illustrate the proposed tools. The show cases used, MagLev launch system, Prandtl Plane, Blended Wing Body, Propeller aircraft and Coandă plane, are a subset of the diverse range of solutions proposed to reduce environmental impact. This framework is a first step on the long way required to arrive at a truly sustainable product design process.
... Modeling environments as complex systems is becoming increasingly common in many studies in the natural and social sciences (35)(36)(37)(38). Complex systems, as they are frequently used in biology, physics, economics, and many other disciplines, refer to multi-scale collections of related objects (a system) that can be identified by their structures and behaviors at different scales of observation. ...
... Market Analysis A large-scale agent-based model for consumer marketing developed in collaboration with a Fortune 50 firm (North et al. 2009). An illustrative agent-based model of a consumer airline market to derive market share for the upcoming year (Kuhn et al. 2010). Agent-based simulation that models the possibilities for a future market in sub-orbital space tourism (Charania et al. 2006). ...
Agent-based modeling and simulation (ABMS) is an approach to modeling systems comprised of individual, autonomous, interacting “agents.” There is much interest in many application problem domains in developing agent-based models. Agent-based modeling offers ways to model individual behaviors and how behaviors affect others in ways that have not been available before. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling by describing the basic ideas of ABMS, discussing some applications, and addressing methods for developing agent-based models.
... ABS is a purely bottom-up approach that abstracts a complex system as a collection of interacting, autonomous agents [2,22]. It provides a number of significant advantages over traditional methods [10,27,37] . ABS is also powerful tools for ''What-if'' scenario analysis [18,33]. ...
Traditional means of urban management are not satisfying the new demands placed by a modern city. Gridding urban management (GUM) seems to be a promising model for urban management in the 21st century. But the GUM must be more than “infrastructure” – it needs to support effective management strategies. This paper outlines our initial attempt of examining urban problems within a GUM framework and studying strategy choices. To this end, we construct an agent-based simulation system, namely DeciUrban, to explore GUM and strategy development. This paper proposes the architecture of DeciUrban and describes its core agent-based model using a model documentation protocol. A simulation of grid inspecting activities in Shanghai GUM is conducted with the purpose of assessing different inspecting strategies. The strategies involve district-first, community-first, cooperative, and random inspecting strategies. The results show how the effectiveness of strategies and the distribution of their impacts can be very different from what one would expect without the benefit of a simulation model. These simulations are presented as a demonstration that DeciUrban can help urban strategy developers understand a multitude of local interactions in cities, explore strategies, and improve community wellbeing without always designing field experiments. To sum up, simulation systems similar to DeciUrban could be valuable tools for addressing the challenges in the further development of GUM.
... Agent-based modeling is a new analytical and computational method envisaged as important in many fields of study that have multi-level system properties, since it gives a better understanding of micro processes and their emergent consequences at macro level [18,49]. This applied method must create a simplified representation of what occurs in reality so that each agent plays the role of an individual as if it is happening in social reality. ...
Purpose
The purpose of this paper is to determine how high-tech firms should choose between independent research and development and technology introduction as well as to ascertain the effects of the three elements of competitive dynamics on the evolution of innovative behavior-based decisions and competitive results.
Design/methodology/approach
This paper describes the construction of an evolutionary game model and a multi-agent-based model of innovative behavior-based decisions by heterogeneous high-tech firms. The models are used to analyze the evolution path and evolutionarily stable strategy of innovative behavior-based decisions. In addition, multi-agent-based simulation is used to gain insight into the effects of competitive dynamics on the dynamic evolution of innovative behavior-based decisions.
Findings
This paper reveals four evolutionary equilibrium states of the innovation behavior-based decisions of high-tech firms. Based on the findings, these overall evolutionary trends are not affected by the timing of competitive market entry or the intensity of competition. In addition, simulated evidence is added that the timing of competitive market entry is an important factor affecting market-leading innovative strategies and dynamic competition results, and competition intensity is closely related to the evolutionary speed of innovation behavior-based decisions.
Originality/value
The key contribution of this paper is its new view of innovative behavior-based decisions from a competitive dynamics perspective. The new competitive dynamics-based framework for innovative behavior-based decisions of high-tech firms proposed in the paper can resolve the problem of obtaining a sustainable competitive advantage for high-tech firms in a competitive dynamics context.
Technological innovations over the years have accentuated the workings in corporate- connected organizations and different application platforms. Hence, a unified management information system (MIS) that can utilize the Web and propel programming developments is required. Software agents, the latest developments in computer software technology, can be utilized to rapidly and effortlessly build integrated information systems. Consequently, 59 research papers on the use of software agents in MIS were identified from top 40 MIS journals published between 2007 and 2017. Then, we reviewed and classified all the research papers according to two categories: application fields and application categories. The application fields consisted of eight sub-groups: manufacturing, telecommunication systems, traffic and transportation management, information filtering and gathering, electronic commerce, business process management, entertainment, and medical care; whereas the application categories consisted of three sub-groups: multi-agent systems, personal assistants, and multi-agent simulation. The research papers were further divided into journal and year of publication, and journal and application field. The objective of our research was to understand the trend of the use software agent technology in MIS by examining the published research paper beside to add knowledge and content to the information system academic discipline.
Designing cooperation-enhancing protocols for large-scale multiagent networked systems has been a grand transdisciplinary challenge. In recent years, tag-based interactions and conformity bias have been studied extensively but separately as two viable mechanisms for cooperation enhancement in such systems. Inspired by recent studies on interaction effects in social dilemmas, we herein develop a hybrid, multiagent-based, co-evolutionary model of tag-mediated cooperation and conformity with conditional and unconditional strategies. Through a series of extensive Monte Carlo simulation experiments, we study four variants of this computational model, finding that under the majority rule, the nonconforming unconditional cooperators and conformity biased transmission of other strategies can lead to global altruistic dominance. Employing a random pinning control mechanism, we further observe that only a small fraction of nonconforming altruists is actually required to drive the system towards a robust persistence of pure altruism. Our analytic results in combination with further computational experiments reveal that spatial structure and nonconformity of cooperators are the two indispensable ingredients for the stable dominance of altruistic behavior in tag-based multiagent systems. Our findings can be beneficial for developing novel cooperation-controlling techniques in distributed self-organizing systems such as peer-to-peer networks or in various social networking and viral marketing technologies.
Emerging Chinese wine market has expanded rapidly in recent years. This paper aims to explore an agent-based modeling method to simulate consumers’ purchase behavior in wine market, China. An agent-based simulation model was built which combined motivation function with decoy effect to simulate consumers’ wine purchase behavior. A survey of 976 valid samples was carried out in China, which meant to estimate key parameters in the simulation model. Then, the experiment was conducted to simulate the decoy effect in China's wine market. An obvious decoy effect was observed, and the agents who changed purchase choices showed similar motivations toward wine brand A and B. The simulation experiment also recognized attribute intervals for creating an effective decoy. The agent-based modeling can simulate consumers’ purchase behavior in different wine markets through adjusting the parameters in simulation model. The experiment shows decoy effect is highly obvious in Chinese wine market. The findings provide valuable insights into Chinese wine consumer behavior and important implications for wine marketers. It is expected that the modeling methods can be served as a model to simulate consumers’ purchase behavior elsewhere in China. It is the first attempt to apply agent-based model and computing simulation parameters to simulate consumers’ purchase behavior in wine market in China.
Online businesses can be represented as a complex interaction of interconnected online users responding to the value proposition of an online company. We propose a Dynamic Agent-Based Modeling framework (DYNAMOD) that aims to explain these complex dynamics. This framework aids in the creation of simulation models that mimic the actual market behavior and perform business forecasting and decision support functions. Through a case study of the largest e-procurement provider in Portugal – Vortal.biz, we simulate their pricing model and analyze revenue impact by optimizing pricing using genetic algorithms. The objective of this research is to propose agent-based model as an effective method to forecast the impact of pricing decisions.
Understanding how to enhance cooperation and coordination in distributed, open, and dynamic multiagent systems has been a grand challenge across disciplines. Knowledge employed in such systems is often limited and heuristic in nature such that cooperation-promoting mechanisms based on trust or reputation become largely unreliable. Although recent studies within the context of tag-based systems reported the emergence of stable cooperation in such uncertain environments, they were limited exclusively to only static interaction structures. Consequently, it remains unknown whether and under what conditions tag-based interactions can promote cooperation in dynamic mobile systems. We herein combine the methods of game theory, evolutionary computing, and agent-based simulation to study the emergence of tag-mediated cooperation in a mobile network with resource diversity. In a series of extensive Monte Carlo simulations, we find that tag-based interactions can give rise to high levels of cooperation even in the presence of different types of contingent mobility. Our model reveals that agent migrations within the system and the invasion of new agents from the outside can have similar effects on the evolution of dominant strategies. Interestingly enough, we observe a previously unreported coexistence of conditional and unconditional strategies in our tag-based model with costly migrations. Differently from earlier studies, we show that this mobility-driven strategy coexistence in our model is not affected by resource limitations or other game-specific factors. Our findings highlight a striking robustness of tag-based cooperation under different mobility regimes, with important consequences for the future design of cooperation-enforcing protocols in large-scale, decentralized, and self-organizing systems such as peer-to-peer or mobile ad-hoc networks.
This paper introduces a Dynamic Agent Based Modelling Framework (DYNAMOD) that is designed for developing Digital Business Simulations. The model is based on literature review of three complementary research areas: Business Models, Business Applications of Agent Based Modeling and Digital Business Characteristics. This Framework is customisable and computationally implements key digital business characteristics including network effects, online and offline word of mouth, pricing strategies, amongst other features of the Digital Business Environment. DYNAMOD can be a generic framework for developing a variety of forcasting and simulation models that can provide a new computational approach to Digital Business Modeling and Analysis.
This paper presents an Agent Based Modeling Framework that can be used to model any internet based business. The model captures the unique characteristics that define how online users interact, share information, and take product adoption decisions. This model can be used to simulate business performance, make business forecasts, and test business strategies. To demonstrate the model, we have applied it to Facebook as well as a leading Portuguese online classifieds website – CustoJusto.pt. Through a number of cases, we have simulated the growth forecasts, its impact by changes in pricing, and changes in the Business Model itself.
The present study aims to propose a model of airline tickets price definition in which consumers and companies dynamically interact in a competitive, multiperiod model, with adaptive decisions over time. Agent-based modeling (ABM) was used in the airfare market to enable companies to observe the demand and adapt their supply conditions. The companies compete through optimal pricing policy regarding the expected consumer's and the competitors' behavior. The results pointed that on the supply side, companies benefit from the reduction of demand elasticity as the travel date approaches. At the same time, the simulations pointed that very frequent price revisions do not seem to be a good policy. On the consumers' side, the yield management is good to consumers, since more consumers can have access to airline tickets. The consumer advantage depends on the airfare purchase time and requires the consumer to be more alert to the price differences over time.
This paper presents the development of a distributed hybrid agent-based (ABS) discrete event simulation (DES) model within the context of emergency medical services (EMS). The existing simulation models of EMS either are considered as a single model or several standalone models that represent different system elements in isolation. The aim of this research is to demonstrate the feasibility of using distributed simulation technology to implement hybrid EMS simulation. This would provide opportunities to study holistically integrated improvement scenarios for emergency medical services and crisis management systems. The case study is based on the London EMS and consists of an ambulance service ABS model and several accident and emergency departments DES models. Both the ABS and the DES models were developed in Repast Simphony toolkit using poRTIco RTI software to achieve communication between them. The results prove that we can use distributed simulation to successfully represent the real system.
Organisations rely on data analytics to process vast quantities of consumer data to inform strategy and policy decisions. Descriptive analytics is commonly used to provide insight into past consumer behaviour. However, greater value can be achieved by predicting future consumer behaviour through predictive and prescriptive analytics. Simulation plays a vital role in facilitating predictive-prescriptive analytics. Here we describe the practical use of an agent-based modelling and simulation approach applied to real-world scenarios, and describe its benefits over traditional econometric, statistical, and spreadsheet approaches. This paper describes two novel agent-based consumer modelling approaches and an associated case-study for each (in water and energy forecasting respectively). The approaches described in this paper are termed characterised consumer modelling involving modelling individual consumers based on their characteristics/type; and personalised consumer modelling involving modelling specific (identified) consumers derived from data on an individual consumer to facilitate personalisation of strategies for that consumer. Validation of the models demonstrates a high level of accuracy and functionality, and suggests that an agent-based simulation approach can answer a range of complex consumer problems using minimal consumer demand data.
Simulation has been used in operations management discipline for decades, but applications in transportation logistics have been rather scarce. Situation is the same in rapid modelling sub-branch. This is mostly due to the reason of needs in high computing power, sophistication of artificial intelligence, and integration of other data sources to the simulation models. However, with ever increasing global trade and derived transportation needs, economically and ecologically sustainable solutions are sought for. We illustrate challenges in this branch with two agent-based simulation models and with one system dynamics model. These are all based on real life observations and data. Complexity and time of model building is likely to increase with this stated problem area, but rapid modelling part is enabled with different scenarios, optimisation and Monte Carlo features during simulation run, but also with feature to put models online to internet and enable significant user interaction.
Although Asia is at the forefront of global economic growth, its investment environment is very risky and uncertain. Credit ratings are objective opinions about credit worthiness, investment risk, and default probabilities of issues or issuers. To classify credit ratings, analyze their determinants, and provide meaningful decision rules for interested parties, this work proposes an integrated procedure. First, this work adopts an integrated feature-selection approach to select key attributes, and then adopts an objective cumulative probability distribution approach (CPDA) to partition selected condition attributes by applying rough sets local-discretization cuts. This work then applies the rough sets LEM2 algorithm to generate a comprehensible set of decision rules. Finally, this work utilizes a rule filter to eliminate rules with poor support and thereby improve rule quality. The experimental focus was the Asian banking industry. Data were retrieved from a BankScope database that covers 1327 Asian banks. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes and achieving increased accuracy, providing a knowledge-based system for classification of rules for solving credit-rating problems encountered by banks, thereby benefiting interested parties.
In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called ‘school’ which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of ‘school’, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders’ search behavior. By simulated annealing, traders’ search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.
The paper provides an introduction to agent-based modelling and simulation of social processes. Reader is introduced to the worldview underlying agent-based models, some basic terminology, basic properties of agent-based models, as well as to what one can and what cannot expect from such models, particularly when they are applied to social-scientific investigation. Special attention is given to the issues of validation. Classification-ACM-1998: J.4 [Computer Applications]; Social and behavioral sciences - Sociology
This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.
This study reports experimental market power and efficiency
outcomes for a computational wholesale electricity market operating in
the short run under systematically varied concentration and capacity
conditions. The pricing of electricity is determined by means of a
clearinghouse double auction with discriminatory midpoint pricing.
Buyers and sellers use a modified Roth-Erev individual reinforcement
learning algorithm (1995) to determine their price and quantity offers
in each auction round. It is shown that high market efficiency is
generally attained and that market microstructure is strongly predictive
for the relative market power of buyers and sellers, independently of
the values set for the reinforcement learning parameters. Results are
briefly compared against results from an earlier study in which buyers
and sellers instead engage in social mimicry learning via genetic
algorithms
Simulation models are becoming increasingly common in the analysis of critical scientific, policy, and management issues. Such models provide a way to analyze complex systems characterized by both large parameter spaces and nonlinear interactions. Unfortunately, these same characteristics make understanding such models using traditional testing techniques extremely difficult. Here we show how a model's structure and robustness can be validated via a simple, automatic, nonlinear search algorithm designed to actively "break" the model's implications. Using the active nonlinear tests (ANTs) developed here, one can easily probe for key weaknesses in a simulation's structure, and thereby begin to improve and refine its design. We demonstrate ANTs by testing a well-known model of global dynamics (World3), and show how this technique can be used to uncover small, but powerful, nonlinear effects that may highlight vulnerabilities in the original model.
Agent-based modeling and simulation (ABMS) - a way to simulate a large number of choices by individual actors - is one of the most exciting practical developments in business and government modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before - a way for businesses and governments to view the future and to understand and anticipate the likely effects of their decisions on their markets, industries, and territories. It thus promises to have far-reaching effects on the way that businesses and governments in many areas use computers to support practical decision-making. This book has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people; and third, to teach readers how to actually implement ABMS by building agent-based simulations. It aims to be a complete ABMS resource and also provides a complete collection of ABMS business and government applications resources.
The disappearance and formation of states after the end of the Cold War have proved puzzling to both theorists and policy-makers. This lack of conceptual preparation stems from two tendencies in Western thinking. First, the dominant focus on cohesive nation-states as the only actors of world politics obscures crucial differences between the state and the nation. Second, traditional theory usually treats these units as fixed. To circumvent these limitations, this dissertation presents a series of models that separate the state from the nation and/or incorporate these as emergent rather than preconceived actors. The models draw on both formal and critical theories in an attempt to integrate them. This requires methodological innovations ranging from minor deviations from existing deductive models to more unconventional frameworks based on complex adaptive systems simulations. The modeling chapters fall into two main parts, the first focusing on state-formation and the second centering on nation-building. Although the illustrations are drawn mainly from European history, the findings apply to more general phenomena. The first part shows that while structural realist predictions about unit-level invariance hold up under certain circumstances, they are heavily dependent on fierce power competition, which can result in unipolarity instead of the balance of power. The second part, on nationalism, illustrates both long-term trends, such as the convergence of identities on the nation-state from both small-scale and cosmopolitan units, and more short-term processes, such as nationalist mobilization and coordination in multi-ethnic states. Such states' efforts to instill loyalty in their peripheral populations may backfire. Moreover, if the revolutionary movement is culturally split, its identity becomes more inclusive as the power gap in the imperial center's favor increases. It is hoped that the emergent actor approach will help shift attention from the exclusively behavioral focus of conventional International Relations theory toward a truly dynamic perspective that treats the actors of world politics as dependent rather than independent variables.
We start with basic terminology and concepts of modeling, and decompose the art of modeling as a process. This overview of the process helps clarify when we should or should not use simulation models. We discuss some common missteps made by many inexperienced modelers, and propose a concrete approach for avoiding those mistakes. After a quick review of event graphs, which are a very straightforward notation for discrete event systems, we illustrate how an event graph can be translated quite directly to a computer program with the aid of a surprisingly simple library. The resulting programs are easy to implement and computationally are extremely efficient. The first half of the paper focuses principles of modeling, and should be of general interest. The second half will be of interest to students, teachers, and readers who wish to know how simulation models work and how to implement them from the ground up.
A central theme in brand choice is the modeling of the drivers of repatronage behavior. The work reported here focuses on a particular service context—airline repatronage. The distinct problems of model specification in this context are discussed. Various models of airline repatronage are estimated using random effects probit modeling. It is found that although performance perceptions and punctuality of the airline have a role to play, in particular in terms of choice on the next purchase occasion, past purchase behavior is the key driver of repatronage. This is evident across the various models that we examine, suggesting that the findings are robust. Implications of the modeling approach and the findings are briefly discussed in relation to the airline context and service marketing more generally.
Recent studies in the accounting literature provide evidence of a market premium whenever firms meet or exceed analysts' earnings forecasts. Financial analysts typically issue revenue forecasts in addition to earnings forecasts. In this study, we draw our motivation from the cue consistency theory to examine whether meeting or exceeding revenue forecasts serves as an additional cue to the market in pricing earnings performance. Consistent with this theory, we show that the market premium (penalty) to meeting or beating (not meeting) earnings forecasts is accentuated when revenue forecasts are also met (not met). Meeting earnings forecasts but not meeting revenue forecasts generally results in a significantly negative market penalty, and the magnitude of the earnings response coefficient jointly depends on whether the earnings and revenue forecasts are met or not. Finally, consistent with previous research, we document a significant association between revenue forecast errors and quarterly abnormal returns. However, we show that after allowing for differential market reactions depending on whether earnings and revenue forecasts are met, this association becomes insignificant. This result suggests that the value of meeting revenue forecasts is arguably of greater importance to market participants than the magnitude of the revenue forecast error.
This paper reports the results of an experiment that examines how analyst forecast accuracy (i.e., how close an analyst's forecast is to realized earnings) and forecast boldness (i.e. how far the analyst's forecast is from the consensus forecast) affect the analyst's perceived credibility and investors' willingness to rely on and purchase the analyst's future reports. We hypothesize and find that forecast boldness magnifies the effect of forecast accuracy on these variables. That is, analysts who provide accurate, bold forecasts experience more positive consequences than those who provide accurate, non-bold forecasts, and analysts who provide inaccurate, bold forecasts experience more negative consequences than those who provide inaccurate, non-bold forecasts. We also find that these effects are not symmetric - the negative consequences of being bold and inaccurate exceed positive consequences of being bold and accurate. Our results are not sensitive to the level of the analyst's prior reputation.
We propose a new method of modeling the relationship between on-time performance and market share in the airline industry. The idea behind the method is that the passengers' decision to remain (use same airline) or switch (use other airlines) at time t depends on whether they have experienced flight delays at time t-1 or not. More specifically, we posit that the passengers who experienced flight delays are more likely to switch airlines for the subsequent flight than those passengers who did not experience delays. To capture such effect, we develop an aggregate-level Markovian type model that estimates the transition probability matrices separately for the passengers who experienced flight delays at time t-1 and for those who did not experience delays. The model was calibrated with the US DOT data. The study results imply that, once experiencing flight delays, passengers are more likely to switch airlines. The results also imply that on-time performance affects a carrier's market share primarily through the passengers' experience, and not though the "advertisement" of performance.
This paper addresses the issue of inter-organizational information sharing alliances and their impacts on firm values from
the perspective of inter-organizational coordination between partners in the airline industry setting. We investigate the
shareholder wealth effects of inter-organizational information-sharing alliance arrangements, using 131 code-sharing agreements
in the airline industry during 1984–1997. Employing event study methodology we found that the information sharing alliances
between similar partners did create positive value in terms of stock returns at the time of alliance announcements to major
US airlines. However, alliances between dissimilar partners resulted in significant losses of shareholder value to the major
airlines. These results strongly support our main hypotheses that information-sharing alliances are successful and the benefits
of such alliances are realized only when the coordination difficulties can be effectively dealt with.
In this tutorial we present techniques for building valid and credible simulation models. Ideas to be discussed include the importance of a definitive problem formulation, discussions with subject-matter experts, interacting with the decision-maker on a regular basis, development of a written assumptions document, structured walk-through of the assumptions document, use of sensitivity analysis to determine important model factors, and comparison of model and system output data for an existing system (if any). Each idea will be illustrated by one or more real-world examples. We will also discuss the difficulty in using formal statistical techniques (e.g., confidence intervals) to validate simulation models.
Some important mechanisms in neuroendocrine-immune (NEI) system are inspired to design a decentralized, evolutionary, scalable, and adaptive system for Web service composition and management. We first abstract a novel intelligent network model inspired from the NEI system. Based on this model, we then propose a method for Web service emergence by designing a bio-entity as an autonomous agent to represent Web service. As such, automatic composition and dynamic management of Web services can be achieved. Also, we build its computation platform which allows the bio-entities to cooperate over Web services and exploits capabilities of their partners. Finally, the simulation results on the platform show that Web service emergence can be achieved through self-organizing, cooperating, and compositing. The proposed method provides a novel solution for intelligent composition and management of Web services.
This paper presents analysis and simulation of meta-reasoning processes based on an agent-based meta-level architecture for strategic reasoning in naval planning. The architecture was designed as a generic agent model and instantiated with decision knowledge acquired from naval domain experts and was specified as an executable agent-based model which has been used to perform a number of simulations. To evaluate the simulation results, relevant properties for the planning decision were identified and formalized. These properties have been validated for the simulation traces.
In recent years, workflow technology has been widely used in business process management. With the increased complexity, uncertainty and risks in business operations, workflow monitoring is gaining growing attention in business process controlling and supervision. However, monitoring functions provided in traditional workflow systems lack flexibility, and provide little support for managing complex and dynamical changes in business process. In this paper, we propose a novel workflow monitoring approach, in which various intelligent agents work together to perform flexible monitoring tasks in an autonomous and collaborative way. By using customized monitoring plan and proactive monitoring process, the workflow monitoring activities can be executed flexibly and efficiently. The application of intelligent agents for such flexible, adaptive and collaborative workflow monitoring is investigated through an intelligent monitoring system in securities trading.
We start with basic terminology and concepts of modeling, and decompose the art of modeling as a process. This overview of the process helps clarify when we should or should not use simulation models. We discuss some common missteps made by many inexperienced modelers, and propose a concrete approach for avoiding those mistakes. After a quick review of event graphs, which are a very straightforward notation for discrete event systems, we illustrate how an event graph can be translated quite directly to a computer program with the aid of a surprisingly simple library. The resulting programs are easy to implement and computationally are extremely efficient. The first half of the paper focuses principles of modeling, and should be of general interest. The second half will be of interest to students, teachers, and readers who wish to know how simulation models work and how to implement them from the ground up.
As e-communities grow in both quality and quantity, their online users require more appropriate tools to suite their needs in such environments. Many such tools are not explicitly needed in real-world communities where humans directly interact with each other. Trust mak- ing and reputation ascription are among the most important examples of such tools. Humans often build trust relationships through interaction or recommendation, and are therefore able to ascribe relevant reputa- tion to those they interact with. However, in online communities the process of trust making and reputation ascription is more complicated. In this paper, we address a special case of the trust making process where community users need to create bonds with those they have not encoun- tered before. This is a common situation in websites such as amazon.com, ebay.com, epionions.com and many others. The model we propose is able to estimate the possible reputation of a given identity in a any new con- text by observing his/her behavior in other communities. Our proposed model employs Dempster-Shafer based valuation networks to develop a global reputation structure and performs a belief propagation technique to infer contextual reputation values. The preliminary evaluation of the proposed model on a dataset collected from epinions.com shows promis- ing results.
Direct marketing is one of the most effective marketing methods with an aim to maximize the customer's lifetime value. Many cost-sensitive learning methods which identify valuable customers to maximize expected profit have been proposed. However, current cost-sensitive methods for profit maximization do not identify how to control the defection probability while maximizing total profits over the customer's lifetime. Unfortunately, optimal marketing actions to maximize profits often perform poorly in minimizing the defection probability due to a conflict between these two objectives . In this paper, we propose the sequential decision making method for profit maximization under the given defection probability in direct marketing. We adopt a Reinforcement Learning algorithm to determine the sequential optimal marketing actions. With this finding, we design a marketing strategy map which helps a marketing manager identify sequential optimal campaigns and the shortest paths toward desirable states. Ultimately, this strategy leads to the ideal design for more effective campaigns.
Interconnected social and environmental systems are the domain of ecological economics, and models can be used to explore feedbacks and adaptations inherent in these systems. Agent-based modeling (ABM) represents autonomous entities, each with dynamic behavior and heterogeneous characteristics. Agents interact with each other and their environment, resulting in emergent outcomes at the macroscale that can be used to quantitatively analyze complex systems. ABM is contributing to research questions in ecological economics in the areas of natural resource management and land-use change, urban systems modeling, market dynamics, changes in consumer attitudes, innovation, and diffusion of technology and management practices, commons dilemmas and self-governance, and psychological aspects to human decision making and behavior change. Frontiers for ABM research in ecological economics involve advancing the empirical calibration and validation of models through mixed methods, including surveys, interviews, participatory modeling, and, notably, experimental economics to test specific decision-making hypotheses. Linking ABM with other modeling techniques at the level of emergent properties will further advance efforts to understand dynamics of social-environmental systems.
Dissertation (Ph.D.)--University of Michigan
Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause–effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non‐current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period.
Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991–1997. This sample was then matched in terms of size and industry to 145 non‐bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included.
In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy/non‐bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.
We present an agent-based computational economics (ACE) model of the wholesale fish market in Marseille. Two of the stylized facts of that market are high loyalty of buyers to sellers, and persistent price dispersion, although it is every day the same population of sellers and buyers that meets in the same market hall. In our ACE model, sellers decide on quantities to supply, prices to ask, and how to treat loyal customers, while buyers decide which sellers to visit, and which prices to accept. Learning takes place through reinforcement. The model explains both stylized facts price dispersion and high loyalty. In a coevolutionary process, buyers learn to become loyal as sellers learn to offer higher utility to loyal buyers, while these sellers, in turn, learn to offer higher utility to loyal buyers as they happen to realize higher gross revenues from loyal buyers. The model also explains the effect of heterogeneity of the buyers. We analyze how this leads to subtle differences in the shopping patterns of the different types of buyers, and how this is related to the behavior of the sellers in the market.
This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.
Simulation models are becoming increasingly common in the analysis of critical scientific, policy, and management issues. Such models provide a way to analyze complex systems characterized by both large parameter spaces and nonlinear interactions. Unfortunately, these same characteristics make understanding such models using traditional testing techniques extremely difficult. Here we show how a model's structure and robustness can be tested via a simple, automatic, nonlinear search algorithm designed to actively "break" the model's implications. Using the active nonlinear tests (ANTs) developed here, one can easily probe for key weaknesses in a simulation's structure, and thereby begin to improve and refine the model's design. We demonstrate ANTs by testing a well-known model of global dynamics (World3), and show how this technique can be used to uncover small, but powerful, nonlinear effects that may highlight vulnerabilities in the original model. This paper has benefited from discu...
- County City
- Denver
City and County of Denver, Colorado Municpal Airport System, Annual
Financial Report, <http://www.flydenver.com/diabiz/stats/financials/reports/
2006_finrpt.pdf>, (accessed December 2007).
Agent based modelling and simulation of social processes. Interdisciplinary Description of Complex Systems
- A Srbljinovic
- O Skunca