Knowledge transfer in a tourism destination: the effects of a network structure

Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.Comment: 15 pages, 2 figures, 2 tables. Forthcoming in: The Service Industries Journal, vol. 30, n. 8, 2010. Special Issue on: Advances in service network analysis v2: addeded and corrected reference

Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models

When is it better to use agent based (AB) models, and when should differential equation (DE) models be used? Where DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity in agent attributes and in the network of interactions among them. Using contagious disease as an example, we contrast the dynamics of AB models with those of the corresponding mean-field DE model, specifically, comparing the standard SEIR model-a nonlinear DE-to an explicit AB model of the same system. We examine both agent heterogeneity and the impact of different network structures, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Surprisingly, in many conditions the AB and DE dynamics are quite similar. Differences between the DE and AB models are not statistically significant on key metrics relevant to public health, including diffusion speed, peak load on health services infrastructure and total disease burden. We explore the conditions under which the AB and DE dynamics differ, and consider implications for managing infectious disease. The results extend beyond epidemiology: from innovation adoption to the spread of rumor and riot to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion

Epidemic Vulnerability Index: Vaccine Dissemination Criteria for Successful Resolution to Epidemics

Vaccination is the preventative measure that effectively decelerates the virus proliferation in a community. A successful response strategy toward pandemics can be obtained through selecting the optimal vaccine distribution route and minimizing the casualties by lowering the death rate and infection rate. In this thesis paper, we propose the Epidemic Vulnerability Index (EVI) that quantifies the potential risk of the subject via analyzing the COVID-19 patient dataset that correlates with mortality and social network analysis that affects the infection rate. We propagate the virus and vaccination in an Agent-based model based on real-world statistics of physical connections and features to 300,000 agents with nine vaccination criteria, including EVI. Vaccination through descending order of EVI has shown the best performance with the numerical outcome of 5.0% lower infection cases, 9.4% lower death cases, and 3.5% lower death rates than the average of other vaccination dissemination criteria

Using information technology to model hand-washing behavior and to improve policies impacting elementary school absenteeism due to influenza

This dissertation revealed several problems on the analysis of in?uenza propagation by indicating how agent-based modeling can be employed to measure the e?ectiveness of control measures and assist in improving health policy to decrease absenteeism among elementary students. The primary question posed was as following: “What is the e?ect of hand hygiene on the possible incidence rates among school children?” After creating an agent-based model representing the in?uenza transmission dynamic, the incidence rates were calculated based on the hand-washing success rates. The statistical results from the simulation model were displayed in graphical format. Finally, the author addressed the issue of measuring validity of the model. The statistical analysis on absenteeism from ?u was performed using data on missed school days in classrooms in one of the local schools in Tippecanoe County, where students exercise hand washing with soap on a regular basis. The analysis also considered data on absenteeism among children who were not required to perform hand washing routinely. This agent-based simulation method is an innocuous and economical approach to model the propagation of respiratory diseases such as in?uenza. It enables the researcher to model individual behaviors and interaction among individuals and their environment. This feature enables the researcher to represent in?uenza transmission dynamic more realistically and to provide in-depth analysis to inquiries for epidemiologist and public health professionals

Data and Design: Advancing Theory for Complex Adaptive Systems

Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour. Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights. In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects. The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed. The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population. The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour

A complex systems approach to constructing better models for managing financial markets and the economy

We outline a vision for an ambitious program to understand the economy and financial markets as a complex evolving system of coupled networks of interacting agents. This is a completely different vision from that currently used in most economic models. This view implies new challenges and opportunities for policy and managing economic crises. The dynamics of such models inherently involve sudden and sometimes dramatic changes of state. Further, the tools and approaches we use emphasize the analysis of crises rather than of calm periods. In this they respond directly to the calls of Governors Bernanke and Trichet for new approaches to macroeconomic modelling.The publication of this work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 284709, a Coordination and Support Action in the Information and Communication Technologies activity area (‘FuturICT’ FET Flagship Pilot Project). Doyne Farmer, Mauro Gallegati and Cars Hommes also acknowledge financial support from the EU-7th framework collaborative project “Complexity Research Initiative for Systemic InstabilitieS (CRISIS)”, grant No. 288501. Cars Hommes acknowledges financial support from the Netherlands Organization for Scientific Research (NWO), project “Understanding Financial Instability through Complex Systems”. None of the above are responsible for errors in this paper.Publicad

On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool

Farmers’ willingness to invest in livestock disease control: the case of voluntary vaccination against bluetongue

Animal health authorities in the European Union nowadays consider voluntary approaches based on a neoliberal model of cost and responsibility sharing as a tool for controlling livestock diseases. Policy makers aim for policies that are soft and optional, and use insights from behavioural economics and social psychology. Voluntary approaches are flexible in terms of legislation and can be effective at lower costs, provided that farmers are willing to participate. In 2008, the Dutch animal health authorities used a voluntary vaccination approach to control an emerging bluetongue epidemic that started end of 2006. Nearly 60,000 holdings with ruminants were already affected by the end of 2007 and experts indicated that transmission could only be stopped through mass vaccination. Farmers were motivated to participate by informational and financial, incentive-based policy instruments. Economic theory predicts that farmers underinvest in private disease control measures in the presence of externalities. These studies, however, assume farmers only consider the private economic motives and that they only can be extrinsically motivated via (monetary) incentives. If the willingness to invest in livestock disease control is also driven by intrinsic and social motives, this could imply that not only financial compensation, but a mix of policy instruments is needed to make voluntary approaches work. The overarching research objective of this thesis was to assess the key determinants of farmers’ willingness to vaccinate against bluetongue and study the impact of different policy designs on the effectiveness of voluntary vaccination approaches to bluetongue disease control. A three-stage research approach was conducted. Two models of decision making, one from economics and one from social psychology, were first applied to the case study to obtain a solid understanding of important perceptions and motivations that farmers have to invest in livestock disease control. These motivations (sometimes incentives) and perceptions were then related to different attributes of a vaccination scheme to have a better understanding of how a higher uptake can be obtained. In the third stage, the effect of the interplay between farmers’ collective behaviour and disease epidemiology on disease rate and vaccination uptake was studied. Expected utility theory was used in combination with decision analysis and Monte Carlo simulation in chapter 2. The economic risk and monetary outcomes of the vaccination decision were considered, intrinsic or social motives ignored. The theoretical expectation from the analysis is that with high probabilities of herd exposure and disease effects at the start of the outbreak the farmer decides to vaccinate. Re-vaccination is uncertain during the course of the epidemic due to a lower probability of herd exposure and enduring protection against infection from previous vaccination. Factors that make re-vaccination more likely to happen are risk-averse behaviour and farm management aimed at the export of heifers. The decision moment – before or during an epidemic – and the characteristics of the disease – endemic, epidemic or emerging – are important factors in perceptions of disease risk. Chapters 3 to 5 used data from a survey that was based on the reasoned action approach. Data were analysed with a variety of statistical, mostly multivariate, techniques. The relative importance of the social-psychological constructs in predicting the intention to participate in a hypothetical reactive vaccination scheme against bluetongue was assessed in chapter 3. It was found that intended vaccination behaviour was mainly explained by farmers’ attitude, but also by social pressures from injunctive and descriptive norms. Perceived behavioural control was the least important predictor of intention. The most influential beliefs underlying the social-psychological constructs were assessed in chapter 4. Results suggested that instrumental beliefs (e.g. risk reduction) as well as experiential beliefs (e.g. animal welfare) were important drivers of the attitude towards vaccination against bluetongue. This indicates that in addition to monetary outcomes of the decision, at least a group of farmers also consider the non-monetary (or non-pecuniary) outcomes. The results further showed that the most influencing referents for the farmer are the veterinarian, his or her family members and colleague dairy farmers (peers). Two influencing control beliefs were associated with the provision of information and perceived trust and confidence in the vaccine safety, effectiveness and government approach to control the disease. The aim of chapter 5 was to explore factors that could explain heterogeneity in farmers’ attitudinal beliefs. In particular, perceived risk, measured by a relative risk attitude and risk perception, and the Big Five personality traits were associated with variability in these beliefs. Conscientiousness discriminated farmers into a group of ‘vaccination intenders’ and non-intenders although it remained somewhat unclear how it relates to the decision problem, as it can be a sense of duty, achievement striving or both. The perceived risk measures were related to the milk production intensity and also discriminated intenders from non-intenders. These differences in perceived risk indicated that farmers might not be commonly risk averse, however, it is important to account for the domain specificity of risk taking behaviour. A survey-based discrete choice experiment was used in chapter 6 to study more deeply farmers’ choices for different voluntary bluetongue vaccination scheme designs. A generalised random utility model of farmers’ behaviour allowed for heterogeneity in motives to invest in bluetongue disease control. Results showed that farmers have private economic motives (incentives) to participate in a vaccination scheme, such as to insure the production risk from disease infection and to maintain the export of heifers. Interaction effects found between social-psychological constructs and specific designs of policy instruments highlighted the importance of perceived trust and confidence in the vaccine safety and effectiveness and in the disease control strategy chosen by animal health authorities. Attitude interacted positively with government communication (information) provided via veterinarians. Descriptive norm interacted positively with a lower perceived probability of adverse effects. This suggests that farmers are more likely to vaccinate if they perceive that others in their social network perform vaccination without experiencing adverse effects. Injunctive norm interacted negatively with a higher level of government subsidy. This suggested a crowding-out mechanism through which subsidization adversely affect farmer’s motivation to comply with the vaccination policy. The interplay between farmers’ collective behaviour and bluetongue disease epidemiology was studied in chapter 7 with an agent-based model. The utility model specification from chapter 6 was used to describe the decision-making process of farmers. Other components that added to the dynamic nature of the model were a social network structure of the diffusion process of sharing information about vaccination status and a susceptible-latent-infectious-recovered model of disease spread. The effectiveness of different bluetongue vaccinations scheme designs was studied as measured by disease rate and vaccination uptake. Results of chapter 7 showed that vaccination schemes that focus more on motivating farmers via informational instruments were somewhat more effective than predicted from the comparative static analysis in chapter 6. Motivation via financial incentives resulted in a somewhat lower effectiveness than was predicted from that same model. This might be explained as an emergent effect that evolves under specific vaccination scheme designs from the interactions between farmers themselves and with the environment from which they observe the progress of the disease. These schemes focus more on serving the information needs of farmers and raising the perceived trust and confidence in the disease control approach rather than on incentivising with higher levels of subsidy. Three themes for livestock disease control emerged from the synthesis of the results in chapter 8, which were subsequently discussed in relation to the wider economic and (social) psychological literature. These themes coincide with shortcoming of the standard economic model of rational choice to describe and predict behaviour. The first theme was about understanding how farmers cope with risk in the context of livestock diseases. The second theme focused on the usefulness of financial compensation as a policy instrument. The third theme discussed the role of trust and social norms. After discussing the implications for policy making, main scientific contributions and suggestions for future research, the chapter concluded that: Dutch dairy farmers who operate large-scale and intensive farms or keep heifers for export are likely to have private economic motives to vaccinate against bluetongue (Chapter 2, 4, 5 and 6).Farmers’ willingness to vaccinate against bluetongue is mostly driven by attitude, followed by perceived social pressures from injunctive norms and descriptive norms. This implies farmers can be motivated intrinsically, extrinsically, or both (Chapter 3).Dutch dairy farmers have intrinsic motives to vaccinate against bluetongue. They do not want to be confronted with animal suffering but want to keep job satisfaction high from working with healthy animals (Chapter 4).Dutch dairy farmers have social motives to vaccinate against bluetongue. They consider what important referents, such as the veterinarian or family members, think they should do and take into account the perceived behaviour of peers (Chapter 3 and 4). Perceived risk, personality traits and past behaviour are important behavioural variables for explaining the heterogeneity in beliefs to vaccinate against bluetongue (Chapter 5). The efficacy of financial, incentive based instruments to motivate to vaccinate against bluetongue is heterogeneous and not necessarily positive for each farmer. They are not effective if farmers already expect a positive net benefit from vaccination or if they crowd-out the motivation to comply with the vaccination policy (Chapter 2, 4, 6, 7). The efficacy of informational policy instruments to motivate farmers to vaccinate against bluetongue is positively affected by farmers’ attitude towards vaccination and in case farmers perceive the communication channels used as credible and trustworthy (Chapter 3, 4, 6). The efficacy of social interaction mechanisms in policy making, such as the perceived social pressuretovaccinateagainstbluetongue,ispositivelyaffectedbyfarmers’trustandconfidence in the government approach to control the disease (Chapter 4, 6, 7).</p

Coarse Graining of Agent-Based Models and Spatio-Temporal Modeling of Spreading Processes

Agent-based models play a central role in modeling social spreading processes, in part because they allow detailed representation of interactions between individuals while integrating data on real-world processes. However, the resulting models are often too complex for a formal analysis and usually require high simulation e ort. In this thesis, based on general remarks on theoretical concepts such as stochastic dynamics and Markov processes, we have rst presented some new theoretical results on the e cient simulation and model reduction of agent-based models. Among these results are an event-based simulation algorithm for ABMs and a model reduction approach based on a projection of the state space and the utilization of convergence results to approximate agent-based models by less complex metapopulation models that can be simulated with much less e ort. Assuming metastability of the agent system, this approach preserves important model characteristics whith a low approximation error. In relation to this background a number of applications of agent-based models have been discussed. Of these, some are of fundamental structure, including a model to achieve global goals with local information, and others concern concrete spreading processes in prehistoric and contemporary societies. A focus among the applications is the spreading and development of culture and innovations in ancient times, both on a conceptual level and with reference to a concrete application case, the spread of the woolly sheep to Europe. In this context, the presented models have been developed through interdisciplinary cooperation and by taking into account archaeological, anthropological as well as geographical data in order to be able to depict the mobility and interactions of nomads, such as hunter-gatherers or shepherds, as realistically as possible. An important aspect that was discussed is the challenges posed by the prehistoric context, both in model parameterization and in validation and interpretation of the results. The comparison with current modeling scenarios is discussed with reference to the application area of epidemic spreading. Speci cally, the di erences in the assumptions about agent mobility and in the availability and reproducibility of data relevant to the model construction and analysis are highlighted. In the analysis of the models, we focused in particular on the identi cation of metastable processes through the application of clustering methods, including a novel approach that exploits the speci c structure of the agent-based models we have presented. Based on this analysis, possibilities for model reduction were discussed, which allow to generate additional data on macroscopic properties and mesoscopic structures of the models with low e ort. Especially, the generation of relevant statistics about critical transitions and other rare events is enabled by the reduced model complexity.Agentenbasierte Modelle spielen bei der Modellierung sozialer Ausbreitungsprozesse eine zentrale Rolle, da sie unter anderem die Interaktionen zwischen Individuen detailliert abbilden und Daten über reale Prozesse integrieren können. Die resultierenden Modelle sind jedoch häufig zu komplex für eine formale Analyse und in der Regel mit einem hohen Simulationsaufwand verbunden. In dieser Arbeit werden zunächst, aufbauend auf allgemeinen Ausführungen zu theoretischen Konzepten wie stochastischer Dynamik und Markov-Prozessen, einige neue theoretische Ergebnisse zur Simulation und Modellreduktion von agentenbasierten Modellen vorgestellt. Hervorzuheben ist dabei ein auf einer Zustandsraumprojektion basierender Ansatz zur Approximation agentenbasierter Modelle durch weniger komplexe Metapopulationsmodelle, die mit deutlich geringerem Aufwand simuliert werden können. Unter der Voraussetzung der Metastabilität bleiben dabei wichtige Modelleigenschaften bei geringem Approximationsfehler erhalten. In diesem Zusammenhang und im Anschluss daran werden eine Reihe von Anwendungen agentenbasierter Modelle diskutiert. Einige davon sind grundlegender Natur, darunter ein Modell zur Erreichung globaler Ziele mit lokalen Informationen, andere betreffen konkrete Ausbreitungsprozesse in prähistorischen und zeitgenössischen Gesellschaften. Ein Schwerpunkt unter den Anwendungsbereichen ist die Ausbreitung und Entwicklung von Kultur und Innovationen in der Antike, sowohl auf konzeptioneller Ebene als auch in Bezug auf einen konkreten Anwendungsfall, die Ausbreitung des Wollschafs nach Europa. Dabei wurden die vorgestellten Modelle in interdisziplinärer Kooperation und unter Berücksichtigung archäologischer, anthropologischer und geographischer Daten entwickelt, um die Mobilität und Interaktionen von Nomaden wie Jägern und Sammlern oder Hirten möglichst realitätsnah abbilden zu können. Ein wichtiger Aspekt, der diskutiert wird, sind die Herausforderungen, die sich aus dem prähistorischen Kontext sowohl für die Modellparametrisierung als auch für die Validierung und Interpretation der Ergebnisse ergeben. Der Vergleich mit aktuellen Modellierungsszenarien wird in Bezug auf das Anwendungsgebiet der Infektionsausbreitung diskutiert. Dabei werden insbesondere die Unterschiede in den Annahmen zur Mobilität der Agenten und in der Verfügbarkeit und Reproduzierbarkeit der für die Modellkonstruktion und -analyse relevanten Daten hervorgehoben. Bei der Analyse der Modelle liegt ein besonderer Schwerpunkt auf der Identifikation metastabiler Prozesse durch die Anwendung von Clusterverfahren, einschließlich eines neuartigen Ansatzes, der die besondere Struktur agentenbasierter Modelle ausnutzt. Darauf aufbauend werden Möglichkeiten der Modellreduktion diskutiert, die es erlauben, mit geringem Aufwand zusätzliche Daten über makroskopische Eigenschaften und mesoskopische Strukturen der Modelle zu erzeugen. Insbesondere die Generierung relevanter Statistiken über kritische Übergänge und andere seltene Ereignisse wird durch die geringere Modellkomplexität erst ermöglicht