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GeoGame analytics – A cyber-enabled petri dish for geographic modeling and simulation
Abstract
As researchers start to conceptualize human-environmental interactions through coupled human and natural systems research, the non-linear, dynamic, heterogeneous, feedback loops that are characteristic of those systems challenges a long-standing Newtonian paradigm of systems reducible to component parts, deterministic behavior, and the existence of equilibrium. As an alternative, complex systems researchers often use agent-based models (ABM) or multi-agent systems (MAS) to model and simulate complexity in human-environmental interactions.
This paper briefly reports on the development of a novel cyberinfrastructure portal solution called GeoGames. This computing environment integrates and leverages web-GIS and multiplayer online game technology to enable simulations of real-world scenarios of coupled human and natural systems applicable to anything from cities, urban regions to other human settlements. While there are some similarities between GeoGames and games like SimCity, and Civilization, a fundamental idea underlying the GeoGames approach is the focus on creating an on-line world that mirrors (c.f. Gelernter, 1991) authentic real-world geography, realized by a full range of GIS supported mapping and processing services (Ahlqvist, Loffing, Ramanathan, & Kocher, P).
In the context of our prototype platform we present the emerging area of Spatial Game Analytics (Drachen & Schubert, 2013) that provides an uncharted area for data-intensive geospatial scenario analysis. Our example scenario is a game that models the relationships of land management on hydrology and water quality. Our presentation is illustrated with examples from our own prototype platform that has generated a significant amount of user data on game play decisions and behavior. Exploratory GeoGame analytics are used to mine the spatial behavior of hundreds of players in order to identify how variations in the rules (land use policies) and varying locations (spatial configurations) affect the simulation outcomes.
... Spatial SGs are becoming popular in transportation science (Ahlqvist et al., 2018), where they continue the tradition of studying driver behavior with traffic simulators (Bonsall et al., 1997) by establishing the incentives for players behaviors. Studies that exploit SGs for establishing transportation-related ABM have also been recently published (Anand et al., 2016;Le Pira et al., 2017). ...
Knowing when a driver will quit cruising and either leave the area or park at an expensive off-street facility is critical for modeling parking search. We employ a serious game-PARKGAME for estimating the dynamics of drivers' decision making. 49 Participants of a game experiment were involved in three scenarios where they had to arrive on time to a fictional appointment or face monetary penalties, and to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Players played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and nearby parking lot of unlimited capacity. Players' choices to quit or to continue search, as dependent on the search time, were analyzed with an accelerated-failure time (AFT) model. Results show that drivers are mostly risk-averse and quit on-street parking search very soon after potential loses begin to accumulate. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.
... Spatial SGs are becoming popular in transportation science (Ahlqvist et al. 2018), where they continue the tradition of studying driver behavior with traffic simulators (Bonsall et al. 1997) by establishing the incentives for players behaviors. Studies that exploit SGs for establishing transportation-related ABM have also been recently published (Anand et al. 2016;Le Pira et al. 2017). ...
Drivers cruising for scarce on-street parking in city centers create negative externalities, including congestion and pollution. We apply a serious game – PARKGAME – to understand and model drivers’ two intertwined instantaneous parking choices: when to quit cruising and where to cruise. Forty-nine participants took part in a lab experiment in which they had to arrive on time to a fictional appointment or face monetary penalties, and had to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Participants played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and a nearby parking lot of unlimited capacity. Players’ parking choices were analyzed with accelerated-failure time (AFT) and multinomial logistic regression models. Results show that drivers are mostly myopic and risk-averse, and quit their on-street parking search long before the optimal moment. Spatially, drivers are attracted by the lot-destination axis, and their turn choices at junctions comply with a second-order biased random walk. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.
... Results showed that a computer-generated environment could be used in a similar way as photographs to assess landscape visual quality (Swetnam and Korenko 2019). Another study used online geospatial games to simulate the influence of land management on hydrology and water quality (Ahlqvist et al. 2018). Google Earth VR offers both bird's eye views and close-up views in different places around the Earth, although the available locations are quite limited. ...
Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public.
... Early examinations of games in science include Lewis et al (2002), Rhyne (2002), and Zyda (2005), Broad discussions of computer games, cartography, and GIScience are provided in Ahlqvist (2011), and of game design principles in Salen et al (2004). Bainbridge (2007) and Ahlqvist et al (2018) discuss the use of analytics to study phenomena related to game communities. Zyda (2005) draws the link between games and simulation for educational purposes. ...
We employ a game engine-Unity-to create a rockfall simulation that integrates high resolution slope data from LiDAR scanning and photogrammetry, simulates hundreds of rocks moving on a slope, and provides outputs relevant to geological engineering decision support. This approach, in contrast to existing rockfall simulation tools that are specific to limited slope types, is general, extensible, and robust. The use of a game engine as a simulator for surface geological processes represents a novel approach to geocomputation that leverages the physics simulation capability of games and the powerful terrain representation and visualization capabilities of 3D game engines.
... Integrative research on ecological-risk management often depends on the use of appropriate tools or models, and it has been increasingly attracting the attention of researchers. These researchers in this field have made valuable contributions such as multi-agent systems for the decision-making processes involved in forest management (Xu et al., 2015;Ahlqvist et al., 2018), artificial intelligence models for simulating and predicting the risks of a land ecosystem (Liu et al., 2018;Tsai et al., 2018), multi-criteria decision-making models for risk-zoning and management of large areas (Li et al., 2014b;Gallego et al., 2019;Souissi et al., 2019), designs for an integrated risk index that can serve as a warning for implementing the appropriate regional development strategies (Partl et al., 2017;Wang et al., 2019), and environmental risk mapping as guidance for risk minimisation in risk management and decision making (Ma et al., 2013;Maldonado et al., 2016;Petus et al., 2016). ...
Prevention of ecological risks in land ecosystems is crucial for environmental protection and sustainable land use. With increasingly severe land degradation, new and effective methods must be developed for the management of ecological risks. In this study, a conceptual decision-making model in ecological risk prevention was developed using the Bayesian belief network with a geographic information system (GIS) for the regional-scale land ecosystem in the traditional mining city of Daye in Central China. Based on the results of a sensitivity analysis, the variable of eco-resilience reduction was identified as the most sensitive to habitat removal with the highest mutual information at 0.71. The two variables of soil pollution and water-quality deterioration were selected for a cross-validation analysis, and the changes in both the calibration and validation performance were very small. The scenarios we considered based on the interests of various stakeholders presented the spatial distribution of the following regulative effects of various management measures on a regional scale: (1) the variable of urbanisation showed that the probability of 11.5 % of all the grids decreased at a high state over an area of 177 km²; (2) the variable of mining showed that the probability of 35.5 % of the all the grids at a high state decreased, over an area of 554 km²; (3) the variable of habitat removal showed that the probability of 6.7 % of all the grids at a high state decreased, over an area of 87 km²; and (4) the variable of health threats showed that the probability of 8.4 % of all the grids at a high state decreased, over an area of 135 km². The Bayesian-network-GIS based tools can support the decision-making process used for ecological-risk prevention in land ecosystems.
... With the development of computer-based technologies, the 3D aspects could be represented more effectively within Geographic Information Systems (GIS) and Virtual Reality (VR) environments (Paar, 2006;Kuliga, Thrash, Dalton, & Holscher, 2015). This potential has recently been fully realised for wind farm assessments by Rafiee, Van der Male, Dias, and Scholten (2017) and in land management in the GeoGame platform developed by Ahlqvist, Khodke, and Ramnath (2018). In both these cases, the spatial data handling capabilities of GIS have been fully integrated within game engine software. ...
This paper investigates and reflects upon the use of digital game landscapes as tools for visual quality assessment. It explores three distinct but related questions. First, can game landscapes engage the missing "young-voice" in landscape evaluations? Second, is it possible to represent the reality of typical landscape vistas? Third, does familiarity with such virtual reality environments impact on overall landscape ratings? This research draws on empirical work undertaken for the Welsh Government to evaluate the impact of their agri-environmental scheme Glastir on the rural landscapes of Wales. This project employed a new, GIS-enabled method to evaluate visual landscape quality, tested using an online photographic preference survey. Whilst the survey was successful, receiving over 2200 responses, young people (<25 years) were significantly underrepresented in the self-selecting sample. To address this gap, we stepped out of the real-world landscapes that most geographers are comfortable with, into the virtual landscapes of gaming. Our response was to create a virtual Welsh landscape which could be navigated in games software and manipulated to mimic landscape changes. A second survey incorporating images of this virtual landscape was first targeted at computer games design students and then secondly to the wider public, with both groups undertaking the same assessment. Overall >70% of respondents were highly satisfied with the quality of the landscape visualisations. Of those who had visited rural Wales before, 64% gave a rating of at least 7 out of 10 for its representativeness. No significant differences in overall landscape ratings were observed between the two groups which is helpful as it indicates that gaming familiarity would not preclude the use of such landscape visualisations in public consultation exercises. This paper considers results from this pilot study and discusses the visual accuracy of the Welsh landscape created. Wider methodological issues are outlined alongside some of the interdisciplinary challenges involved in the construction of the landscape visualisation.
With the increase of worldwide population, the environment has suffered intensely, where climate change issues are some of the top priorities for decision-makers worldwide. The extreme events recently witnessed have affected a countless number of people, with the loss of lives, properties and livelihood. Resourcing to technologies that facilitate decision-making is one of the viable options to correct our current path into the future. By using agent-based modelling coupled with geographical information systems, an effective platform can be created to design a bottom-up approach in policy creation and planning. This review paper analyses studies that have applied this methodology, yielding a vast range of results that were divided into nine different sustainable development categories: natural resources, social participation and trust, material welfare and economy, climate and energy, technology, biodiversity and landscape, mobility, safety and land development. The number of results shows that these studies are extremely relevant to the sustainable development topic. Although having manifested some limitations related to the transparency of the model design or validation issues, the application of agent-based modelling coupled with geographical information systems proved a competent tool for implementation in environmental and sustainability subjects.
The term ‘smart cities’ is contested: its interpretation is becoming ever broader, often to accommodate commercial interests. Since cities are made up of individuals, all of whom are guided by their own world views and attitudes, the residual question is not ‘what should we do?’ but ‘how should we do it and how should we encourage and enable everyone to join in?’ By exploring the ways that gamification can be used to understand the effects of ‘smart initiatives’ on cities and their operation, it was concluded that gaming has considerable potential to affect individual and societal practices by profoundly influencing the gamers themselves, while technology and the game design itself play a central role to how gamification is implemented and used. This paper proposes one way of both creating cities to which citizens aspire and delivering a beneficial change in attitudes and behaviours to make such cities work. We propose that way-finding games should be developed as the most appropriate tools for participation. Designing such serious games with sustainability, resilience and liveability agendas in mind, encouraging widespread citizen participation as gamers, and taking cognisance of the outcomes would lead to both smarter citizens and smarter cities.
In order to produce precise outcomes, most spatial simulations require great volumes of data input, intensive data preparation or programming skills. Consequently, high quality spatial simulations are usually not applied in practice in municipalities of smaller size (<25,000 citizens). This study aims to provide an easy-to-use tool for smaller urban administrations. A web application was developed which requires a minimum of manual user input for automated data preparation. Gamified elements aim to encourage the user to experience the underlying mechanisms of the system under alternating planning scenarios. These mechanisms are modelled as a combination of spatially implicit and explicit rules which can be derived from dependencies in the data itself. Spatial attractiveness was modelled as a function of accessibility of facilities, ground value, soil sealing, traffic intensity and noise pollution in a multi-variate spatial autoregressive regression analysis. A case study for the city center of Herdecke (Germany) revealed that the establishment of a new event location in the northern part of the study area was most appropriate for increasing spatial attractiveness. The presented easy-to-use tool is suitable for practical application in everyday administrative processes of smaller municipalities and thereby contributes to more applied sustainable urban planning.
There is an increasing drive to combine agent-based models with empirical methods. An overview is provided of the various empirical methods that are used for different kinds of questions. Four categories of empirical approaches are identified in which agent-based models have been empirically tested: case studies, stylized facts, role-playing games, and laboratory experiments. We discuss how these different types of empirical studies can be combined. The various ways empirical techniques are used illustrate the main challenges of contemporary social sciences: (1) how to develop models that are generalizable and still applicable in specific cases, and (2) how to scale up the processes of interactions of a few agents to interactions among many agents.
Agent-based models tend to be more and more complex. In order to cope with this increase of complexity, powerful modeling and simulation tools are required. These last years have seen the development of several platforms dedicated to the development of agent-based mod- els. While some of them are still limited to the development of simple models, others allow to develop rich and complex models. Among them, the GAMA modeling and simulation platform is aimed at supporting the design of spatialized, multiple-paradigms and multiple-scales mod- els. Several papers have already introduced GAMA, notably in earlier PRIMA conferences, and we would like, in this paper, to introduce the new features provided by GAMA 1.6, the latest revision to date of the platform. In particular, we present its capabilities concerning the tight combination of 3D visualization, GIS data management, and multi-level modeling. In addition, we present some examples of real projects that rely on GAMA to develop complex models.
Coupled human and natural systems (CHANS) manifest various complexities such as heterogeneity, nonlinearity, feedback, and emergence. Humans play a critical role in affecting such systems and in giving rise to various environmental consequences, which may in turn affect future human decisions and behavior. In light of complexity theory and its application in CHANS, this paper reviews various decision models used in agent based simulations of CHANS dynamics, discussing their strengths and weaknesses. This paper concludes by advocating development of more process-based decision models as well as protocols or architectures that facilitate better modeling of human decisions in various CHANS.
An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and –research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and –design.
Why pay attention to games? For starters, games are the "medium of choice" for many Millennials, with broad participation among the 30 and under population. Although part of a web of new media, technology, and social shifts, games are the quintessential site for examining these changes. Game cultures feature participation in a collective intelligence, blur the distinction between the production and consumption of information, emphasize expertise rather than status, and promote international and cross-cultural media and communities. Most of these characteristics are foreign, or run counter to print-era institutions such as libraries. At the same time, game cultures promote various types of information literacy, develop information seeking habits and production practices (like writing), and require good, old-fashioned research skills, albeit using a wide spectrum of content. In short, librarians cannot afford to ignore garners.
As video games become more popular, there is an urge for procedures that can support the analysis and understanding of players’ behaviors within game environments. Such data would inform game and level designers of game design issues that should be fixed or improved upon. By logging user-initiated events in video games, analysts have exhaustive information regarding players’ actions within games. However, visualizing such data is a challenging task due to the amount of data one has to deal with; the necessity of a deep understanding of the game and players’ possible actions within the game plus a deep understanding of questions one wants to answer; the computation that has to be done on the data; and the limitations and/or complexities of current analysis tools. In this paper, we present a new visualization system that allows analysts to build visualization and interact with telemetry data, to identify patterns and identify game design issues efficiently. Besides the system itself, we propose a new approach to visualize players’ behavior that has not been explored so far. For example, instead of using heat maps to visualize a single metric (e.g. deaths), our system allows analysts to superimpose and visualize a series of actions players take in the game. This is especially important when one should understand cause and effect within the game. We present examples of the visualizations using an RPG game, Dragon Age Origins (BioWare/EA, 2009). It should be noted that the system is currently under development and testing with analysts working at BioWare.
This paper describes how the Cormas platform has been used for 12 years as an artefact to foster learning about agent-based simulation for renewable resource management. Among the existing generic agent-based simulation platforms, Cormas occupies a tiny, yet lively, place. Thanks to regular training sessions and an electronic forum, a community of users has been gradually established that has enabled a sharing of ideas, practices and knowledge, and the emergence of a genuine community of practice whose members are particularly interested in participatory agent-based simulation.
In today’s increasingly “flat” world of globalization (Friedman 2005), the need for a scientifically literate citizenry has grown more urgent. Yet, by some measures, we have done a poor job
at fostering scientific habits of mind in schools. Recent research on informal games-based learning indicates that such technologies and the communities they evoke
may be one viable alternative—not as a substitute for teachers and classrooms, but as an alternative to textbooks and science
labs. This paper presents empirical evidence about the potential of games for fostering scientific habits of mind. In particular,
we examine the scientific habits of mind and dispositions that characterize online discussion forums of the massively multiplayer
online game World of Warcraft. Eighty-six percent of the forum discussions were posts engaged in “social knowledge construction” rather than social banter.
Over half of the posts evidenced systems based reasoning, one in ten evidenced model-based reasoning, and 65% displayed an
evaluative epistemology in which knowledge is treated as an open-ended process of evaluation and argument.
We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.
The agent-based modeling is now widely used to study complex systems. Its ability to represent several levels of interaction along a detailed (complex) environment representation favored such a development. However, in many models, these capabilities are not fully used. Indeed, only simple, usually discrete, environment representation and one level of interaction (rarely two or three) are considered in most of the agent-based models. The major reason behind this fact is the lack of simulation platforms assisting the work of modelers in these domains. To tackle this problem, we developed a new simulation platform, GAMA. This platform allows modelers to define spatially explicit and multi-levels models. In particular, it integrates powerful tools coming from Geographic Information Systems (GIS) and Data Mining easing the modeling and analysis efforts. In this paper, we present how this platform addresses these issues and how such tools are available right out of the box to modelers.
Geographic data and tools are essential in all aspects of emergency management: preparedness, response, recovery, and mitigation. Geographic information created by amateur citizens, often known as volunteered geographic information, has recently provided an interesting alternative to traditional authoritative information from mapping agencies and corporations, and several recent papers have provided the beginnings of a literature on the more fundamental issues raised by this new source. Data quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data. During emergencies time is the essence, and the risks associated with volunteered information are often outweighed by the benefits of its use. An example is discussed using the four wildfires that impacted the Santa Barbara area in 2007–2009, and lessons are drawn.
Integrated studies of coupled human and natural systems reveal new and complex patterns and processes not evident when studied
by social or natural scientists separately. Synthesis of six case studies from around the world shows that couplings between
human and natural systems vary across space, time, and organizational units. They also exhibit nonlinear dynamics with thresholds,
reciprocal feedback loops, time lags, resilience, heterogeneity, and surprises. Furthermore, past couplings have legacy effects
on present conditions and future possibilities.
Perhaps the most beloved visualization of player behavior is the heatmap, which offers clear and intuitive feedback about the spatial behavior of players. Heatmaps are, however, only the tip of a very deep iceberg of the area – we here will refer to as spatial game analytics – and it has a lot more to offer than heatmaps, not the least a strong explanatory power for deciphering and understanding player behavior. Here we take a plunge into these deep waters, exploring what is already being done and what can be done within this area and to a lesser degree visualization – which is further explored in the following chapters (Chaps. 18 and 19).
This book introduces the companion modelling approach by presenting the stance that underpins it, the methods and tools used with stakeholders and the specific role of models during the process. It addresses the means to deal with the different levels of decision-making and to take into account the various power relationships. It proposes a methodology to assess the impact of the approach on the stakeholders involved in the process. The book includes 27 case studies and 7 teaching tools that describe the successful use of the approach in a variety of settings or teaching contexts. It is intended for researchers working on rural development or renewable resources management, as well as students and teachers. © 2014 Éditions Quæ, R10, 78026 Versailles cedex, France. All rights are reserved.
A Decision Support System (DSS) for optimal operation of multireservoir systems employs the powerful graphical user interface for the MODSIM river basin network flow model in creating applications for the CSUDP dynamic programming package. The DSS provides capability for graphical construction of multireservoir system networks and object oriented database development for fully dynamic optimization. The interface automatically constructs the required system state dynamics and objective function routines required for the CSUDP model. The DSS provides a powerful adjunct to the MODSIM river basin network model by performing fully dynamic optimization for development of optimal reservoir operational rules. These operational guide-curves can then be input into MODSIM for realistic evaluation of river basin management alternatives. An efficient dynamic programming successive approximations algorithm is applied for systems with up to 30 reservoirs and other system state variables. The DSS is applied to the Mahaweli system in Sri Lanka as a case study.
Accurate modeling of urban growth is an extremely important component of urban geographic studies and is also vital for future urban planning. The trajectories of urban growth can be monitored and modeled by the use of geographic information system techniques, remote sensing data, and statistical analysis. In this study, we couple game theory with an integrated agent-cellular method to develop a model of the major determinants controlling urban development, which not only accounts for socioeconomic driving forces but also captures human actions. Wuhan, the largest city in central China, is undergoing rapid urbanization and is facing uncontrolled urban expansion. The city proper region of Wuhan is selected as the case study area to simulate urban growth during the period between 2003 and 2023. The results indicate that the social conflicts between the different stakeholders in urban development can be identified by utilizing a game tree. The game-theory based agent-cellular model is shown to be more effective than a pure cellular automata model in urban growth simulation. The results also show that, from 2013 to 2023, the urban area of the Wuhan city proper region is predicted to grow to 442.77 km2, which is almost two times the area in 2003. This research is the first study to use empirical data and game theory to analyze the decision-making process in urban development in the Wuhan area.
Land-use patterns can be considered as a consequence of competitions between different land-use types. How to coordinate the competitions is the key to land-use spatial optimization. In order to improve the ability of existing land-use spatial optimization models for addressing local land-use competitions (the competitions on land units), a loosely coupled model based on a genetic algorithm (GA) and game theory is constructed. The GA is repeatedly executed to separately optimize the spatial layout of each land-use type. The land-use status quo is overlaid with the optimization results to find local land-use competitions. The concept of land-use competition zones is introduced in this study. Using the competition zones as the basic units, the model utilizes multi-stakeholder games and the knowledge of land-use planning to coordinate the local land-use competitions. The final solution is obtained after the land-use coordination. Gaoqiao Town, Zhejiang Province is selected as the study area to verify the validity of the model. The experimental results confirm that the model is feasible to undertake land-use spatial optimization and to coordinate the competitions between different land-use types.
This article reports on the initial development of a generic framework for integrating Geographic Information Systems (GIS) with Massive Multi‐player Online Gaming (MMOG) technology to support the integrated modeling of human‐environment resource management and decision‐making. We review Web 2.0 concepts, online maps, and games as key technologies to realize a participatory construction of spatial simulation and decision making practices. Through a design‐based research approach we develop a prototype framework, “GeoGame”, that allows users to play board‐game‐style simulations on top of an online map. Through several iterations we demonstrate the implementation of a range of design artifacts including: real‐time, multi‐user editing of online maps, web services, game lobby, user‐modifiable rules and scenarios building, chat, discussion, and market transactions. Based on observational, analytical, experimental and functional evaluations of design artifacts as well as a literature review, we argue that a MMO GeoGame‐framework offers a viable approach to address the complex dynamics of human‐environmental systems that require a simultaneous reconciliation of both top‐down and bottom‐up decision making where stakeholders are an integral part of a modeling environment. Further research will offer additional insight into the development of social‐environmental models using stakeholder input and the use of such models to explore properties of complex dynamic systems.
Maps and games have a long history of co-evolution, and after many years of parallel and sometimes independent development, we see today a convergence of mapping and gaming technologies. This report presents five broad themes in current-day computer gaming and cartography, highlighting some of the connections between these two dynamic fields and arguing for the potential of combining modern cartographic theory, tools, and practice with gaming approaches.
Complex social-ecological systems (SES) are not amenable to simple mathematical modeling. However, to address critical issues in SES (e.g., understanding ecological resilience/amelioration of poverty) it is necessary to describe such systems in their entirety. Based on empirical knowledge of local stakeholders and experts, we mapped their conceptions of one SES. Modelers codified what actors told us into two models: a local-level model and an overarching multiple-entity description of the system. Looking at these two representations together helps us understand links between the locally specific and other levels of decision taking and vice-versa. This “bimodeling” approach is investigated in one SES in coastal Kenya. © 2014 Wiley Periodicals, Inc. Complexity, 2014
Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Interpretability and reliability of clustering results is vital, as decisions based on them affect game design and thus ultimately revenue. Here case studies are presented focusing on clustering analysis applied to high-dimensionality player behavior telemetry, covering a combined total of 260,000 characters from two major commercial game titles: the Massively Multiplayer Online Role-Playing Game Tera and the multi-player strategy war game Battlefield 2: Bad Company 2. K- means and Simplex Volume Maximization clustering were applied to the two datasets, combined with considerations of the design of the games, resulting in actionable behavioral profiles. Depending on the algorithm different insights into the underlying behavior of the population of the two games are provided.
A tool for participative business process development is the so-called social simulation game method. The social simulation game method is like a ''role-play'' but is tailored to an organisation's own business processes. The main purpose is to develop business processes, i.e., to analyse present processes, to test alternative process modes and to train employees. The authors have carried out and studied simulation games in different industrial organisations over the past ten years, though these have been more focused during the last four years. According to these research results, the participative simulation game method has proven to be an effective facilitator in business process development and training in industrial organisations. This article is based on 32 case studies, including 88 simulation games, which are briefly described. Our primary aim is to describe how the social simulation game method is used in training and participative development for different processes and purposes. Results indicate that the method enables individual learning as well as promotes both single-loop and double-loop types of organisational learning. The simulation game method underscores the problems and development needs of the simulated business processes. The short-term and long-term qualitative (soft) and quantitative (hard) as well as the so-called future results and implications of the cases in implementation are reported and discussed. The concept of tacit and explicit change as a result of the interplay of tacit and explicit knowledge and soft, hard and future results has been created.
Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing.
This paper outlines the ways in which information technologies (ITs) were used in the Haiti relief effort, especially with respect to web-based mapping services. Although there were numerous ways in which this took place, this paper focuses on four in particular: CrisisCamp Haiti, OpenStreetMap, Ushahidi, and GeoCommons. This analysis demonstrates that ITs were a key means through which individuals could make a tangible difference in the work of relief and aid agencies without actually being physically present in Haiti. While not without problems, this effort nevertheless represents a remarkable example of the power and crowdsourced online mapping and the potential for new avenues of interaction between physically distant places that vary tremendously.
This report documents a review of 48 empirical research articles on the effectiveness of instructional games. It also includes summaries of 26 other review articles and 31 theoretical articles on instructional gaming. Eased on this review the following 5 conclusions and 4 recommendations are provided. Conclusions: (1) The empirical research on the instructional effectiveness of games is fragmented, filled with ill defined terms, and plagued with methodological flaws. (2) Some games provide effective instruction for some tasks some of the time, but these results may not be generalizable to other games or instructional programs. (3) No evidence indicates that games are the preferred instructional method in all situations. (4) Instructional games are more effective if they are embedded in instructional programs that include debriefing and feedback. (5) Instructional support during play increases the effectiveness of instructional games. Recommendations: (1) The decision to use a game for instruction should be based on a detailed analysis of learning requirements and tradeoffs among alternate instructional approaches. (2) Program managers and procurement officials should insist that instructional game developers demonstrate how their game will support instructional objectives. (3) Games should be used as adjuncts and aids, not as stand-alone instruction. (4) Instructor-less a roaches (e.g., web-based instruction) must include all "instructor functions."
The MODSIM river basin management model has been extended to GEO-MODSIM for integration with GIS for spatial data base management, analysis, and display. GEO-MODSIM is a custom ArcMap extension that provides the foundation for integrated river basin management. Numerous geodatabase layers are loaded and processed in GEO-MODSIM, including topography, political divisions, hydrography, irrigated fields, soil maps, land use, field measurements, and satellite imagery. Spatial-temporal data bases are also loaded such as NEXRAD precipitation data, water rights, gauging station records, diversions, pumping wells, and monitored surface water locations. These base GIS layers are processed to delineate watersheds, generate geometric networks, and create hydro-networks. Formatted data sets are created for executing external spatially distributed network flow optimization models, groundwater models and water quality models directly from the ArcMap interface. GEO-MODSIM is applied to the Lower Arkansas River basin, Colorado for salinity management and the Imperial Irrigation District, California for water conservation analysis.
Little attention has been given to the psychological and sociological value of play despite its many advantages to guiding the design of interactive multimedia learning environments for children and adults. This paper provides a brief overview of the history, research, and theory related to play. Research from education, psychology, and anthropology suggests that play is a powerful mediator for learning throughout a person's life. The time has come to couple the ever increasing processing capabilities of computers with the advantages of play. The design of hybrid interactive learning environments is suggested based on the constructivist concept of a microworld and supported with elements of both games and simulations.
Multi-agent systems (MAS) open new modelling and analysis perspectives in ecological and social sciences. An original characteristic of the companion modelling (ComMod) approach adopted in this case study is the co-construction and use of a MAS model with and for local stakeholders such as farmers and local administrators. Alternating iteratively field and modelling activities, this approach facilitates collective learning among local stakeholders and between them and the researchers. Combining the use of MAS models with role-playing games (RPG), the described experiment aimed to facilitate collective decision-making in a socially heterogeneous community of small farmers in mountainous Northern Thailand about the local rules for the allocation of rural credit to allow a more equitable and extensive process of expansion of non-erosive perennial crops in a watershed prone to erosion. This paper presents the MAS model and the results of a series of simulations exploring the ecological, social and economic effects of various rules for formal and informal credit suggested by the villagers-participants. Six scenarios considered as pertinent to further explore the participants' suggestions were defined based on different combinations among the following three variables: (i) Duration for the reimbursement of loans, (ii) Mode of allocation of formal credit among three different types of farms, (iii) Configuration of networks of acquaintances for access to informal credit.
Complexity theory has captured the attention of the scientific community to the extent where its proponents tout it as a dominant scientific trend. Geographers, and environmental, human, and regional planners have applied complexity theory to topics ranging from cultural transmission and economic growth to the braiding of rivers. While such a wide array of applications is heartening because it speaks to the utility of complexity theory, it is necessary to move beyond the hyperbole and critically examine the nature of complexity research. The author therefore provides an overview of the evolution of complexity research, establishes a preliminary typology of complexity approaches with their advantages and drawbacks, and identifies areas of further research.
System models in agriculture and natural resource management are usually developed by researchers to advise policy makers on the likely outcomes of alternative scenarios. Except for data collection, stakeholders—like farm households—are rarely involved in the research process. Companion modeling (ComMod) has been developed as a modeling approach to include various stakeholders in a continuous feedback loop between researchers and stakeholders. Whereas other ComMod approaches have used role playing games as an interface between researchers and stakeholders—assuming that stakeholders have difficulties understanding a computer model—this paper explores the potential of a participatory modeling approach in which stakeholders are directly confronted with the model by assessing its assumptions, interpreting simulation results, and suggesting scenarios.
Evaluating DVE topology management and message propagation schemes requires avatar movement models. Most models are based on reasoned assumptions rather than measured data, potentially biasing evaluation. We measured player movement in World of Warcraft battlegrounds, and compared our observations against common assumptions about player avatar movement and navigation. We found that when modeling a highly interactive DVE such as a battleground, a waypoint model is not sufficient to describe most avatar movement. We were surprised to find that despite game incentives for grouping, the majority of avatar movement between objectives is individual, not grouped. Finally, we found that a hotspot-based model for avatar movement is consistent with our traces.
The use of the related terms "agent-based", "multi-agent", "software agent" and "intelligent agent" have witnessed significant growth in the Geographic Information Science (GIScience) literature in the past decade. These terms usually refer to both artificial life agents that simulate human and animal behavior and software agents that support human-computer interactions. In this article we first comprehensively review both types of agents. Then we argue that both these categories of agents borrow from Artificial Intelligence (AI) research, requiring them to share the characteristics of and be similar to AI agents. We also argue that geospatial agents form a distinct category of AI agents because they are explicit about geography and geographic data models. Our overall goal is to first capture the diversity of, and then define and categorize GIScience agent research into geospatial agents, thereby capturing the diversity of agent-oriented architectures and applications that have been developed in the recent past to present a holistic review of geospatial agents.
The practice of crowdsourcing is transforming the Web and giving rise to a new field.
Agent-based simulation (ABS) is being increasingly used in environmental management. However, the efficient and effective use of ABS for environmental modelling is hindered by the fact that there is no fixed and clear definition of what an ABS is or even what an agent should be. Terminology has proliferated and definitions of agency have been drawn from an application area (Distributed Artificial Intelligence) which is not wholly relevant to the task of environmental simulation. This situation leaves modellers with little practical support for clearly identifying ABS techniques and how to implement them.This paper is intended to provide an overview of agent-based simulation in environmental modelling so that modellers can link their requirements to the current state of the art in the techniques that are currently used to satisfy them. Terminology is clarified and then simplified to two key existing terms, agent-based modelling and multi-agent simulation, which represent subtly different approaches to ABS, reflected in their respective artificial life (A-life) and distributed artificial intelligence roots. A representative set of case studies are reviewed, from which a classification scheme is developed as a stepping-stone to developing a taxonomy. The taxonomy can then be used by modellers to match ABS techniques to their requirements.
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