S. Fajardo, G. Hofstede, M. D. Vries, M. Kramer, Andrés Bernal
Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.
{"title":"Gregarious Behavior, Human Colonization and Social Differentiation: An Agent-Based Model","authors":"S. Fajardo, G. Hofstede, M. D. Vries, M. Kramer, Andrés Bernal","doi":"10.31235/osf.io/7z6xp","DOIUrl":"https://doi.org/10.31235/osf.io/7z6xp","url":null,"abstract":"Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90698362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.
{"title":"Finding Core Members of Cooperative Games using Agent-Based Modeling","authors":"Daniele Vernon-Bido, Andrew J. Collins","doi":"10.18564/jasss.4457","DOIUrl":"https://doi.org/10.18564/jasss.4457","url":null,"abstract":"Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88756015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-05DOI: 10.1101/2020.07.02.20145052
Matt Koehler, David M. Slater, G. Jacyna, James R. Thompson
As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to the slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19.
{"title":"Modeling COVID-19 for lifting non-pharmaceutical interventions","authors":"Matt Koehler, David M. Slater, G. Jacyna, James R. Thompson","doi":"10.1101/2020.07.02.20145052","DOIUrl":"https://doi.org/10.1101/2020.07.02.20145052","url":null,"abstract":"As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to the slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79496444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Carletti, A. Guarino, A. Guazzini, Federica Stefanelli
: People tend to form groups when they have to solve difficult problems because groups seem to have betterproblem-solvingcapabilitiesthanindividuals. Indeed, duringtheirevolution, humanbeingslearnedthat cooperation is frequently an optimal strategy to solve hard problems both quickly and accurately. The ability of a group to determine a solution to a given problem, once group members alone cannot, has been called “Collective Intelligence". Such emergent property of the group as a whole is the result of a complex interaction between many factors. Here, we propose a simple and analytically solvable model disentangling the direct link between collective intelligence and the average intelligence of group members. We found that there is a non-linear relation between the collective intelligence of a group and the average intelligence quotient of its members depending on task difficulty. We found three regimes as follows: for simple tasks, the level of collective intelligence of a group is a decreasing function of teammates’ intelligence quotient; when tasks have intermediate difficulties, the relation between collective intelligence and intelligence quotient shows a non-monotone behaviour; for complex tasks, the level of collective intelligence of a group monotonically increases withteammates’intelligencequotientwithphasetransitionsemergingwhenvaryingthelatter’slevel. Although simple and abstract, our model paves the way for future experimental explorations of the link between task complexity, individual intelligence and group performance.
{"title":"Problem Solving: When Groups Perform Better Than Teammates","authors":"T. Carletti, A. Guarino, A. Guazzini, Federica Stefanelli","doi":"10.18564/jasss.4292","DOIUrl":"https://doi.org/10.18564/jasss.4292","url":null,"abstract":": People tend to form groups when they have to solve difficult problems because groups seem to have betterproblem-solvingcapabilitiesthanindividuals. Indeed, duringtheirevolution, humanbeingslearnedthat cooperation is frequently an optimal strategy to solve hard problems both quickly and accurately. The ability of a group to determine a solution to a given problem, once group members alone cannot, has been called “Collective Intelligence\". Such emergent property of the group as a whole is the result of a complex interaction between many factors. Here, we propose a simple and analytically solvable model disentangling the direct link between collective intelligence and the average intelligence of group members. We found that there is a non-linear relation between the collective intelligence of a group and the average intelligence quotient of its members depending on task difficulty. We found three regimes as follows: for simple tasks, the level of collective intelligence of a group is a decreasing function of teammates’ intelligence quotient; when tasks have intermediate difficulties, the relation between collective intelligence and intelligence quotient shows a non-monotone behaviour; for complex tasks, the level of collective intelligence of a group monotonically increases withteammates’intelligencequotientwithphasetransitionsemergingwhenvaryingthelatter’slevel. Although simple and abstract, our model paves the way for future experimental explorations of the link between task complexity, individual intelligence and group performance.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75336849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain can be viewed as a public ledger maintained collectively by a large number of participators based on consensus protocol. We are interested in how di erent consensus protocols and trade network topologies a ect the performance of a blockchain system, which has not been studied in the literature yet. In this paper, we proposed an agent-basedmodel consisting ofmultiple trader andminer agents, and one system agent. We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS).We also examined three common trade network topologies: random, small-world, and scale-free. We find that both consensus protocol and trade network topology can impact the performance of blockchain system. PoS and DPoS are generally better than PoW in terms of increasing trade e iciency and equalizing wealth. Besides, scale-free trade network is not favorable because its trade e iciency is quite low, which moderates the price fluctuation and wealth inequality. Since connectivity inequality determines trade e iciency and wealth inequality, it is crucial to increase the connectivity among participants by means of not only using better consensus protocols such as PoS or DPoS, but also incentivizing apathetic or newly-joined participants to link with others. We suggest that our findings could be useful to the designers, practitioner and researchers of blockchain system and token economy.
{"title":"Impacts of Consensus Protocols and Trade Network Topologies on Blockchain System Performance","authors":"Xianhua Wei, Aiya Li, Zhou He","doi":"10.18564/jasss.4289","DOIUrl":"https://doi.org/10.18564/jasss.4289","url":null,"abstract":"Blockchain can be viewed as a public ledger maintained collectively by a large number of participators based on consensus protocol. We are interested in how di erent consensus protocols and trade network topologies a ect the performance of a blockchain system, which has not been studied in the literature yet. In this paper, we proposed an agent-basedmodel consisting ofmultiple trader andminer agents, and one system agent. We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS).We also examined three common trade network topologies: random, small-world, and scale-free. We find that both consensus protocol and trade network topology can impact the performance of blockchain system. PoS and DPoS are generally better than PoW in terms of increasing trade e iciency and equalizing wealth. Besides, scale-free trade network is not favorable because its trade e iciency is quite low, which moderates the price fluctuation and wealth inequality. Since connectivity inequality determines trade e iciency and wealth inequality, it is crucial to increase the connectivity among participants by means of not only using better consensus protocols such as PoS or DPoS, but also incentivizing apathetic or newly-joined participants to link with others. We suggest that our findings could be useful to the designers, practitioner and researchers of blockchain system and token economy.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89737041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social and computational archaeology focuses largely on the study of past societies and the evolution of human behaviour. At the same time, agent-basedmodels (ABMs) allow the e icientmodeling of human agency, and the quantitative representation and exploration of specific properties and patterns in archaeological information. In this work we put forward a novel agent-based trading model, for simulating the exchange anddistributionof resources across settlements inpast societies. Themodel is part of a broader ABMpopulated with autonomous, utility-seeking agents corresponding to households; with the ability to employ any spatial interaction model of choice. As such, it allows the study of the settlements’ trading ability and power, given their geo-location and their position within the trading network, and the structural properties of the network itself. As a case study we use the Minoan society during the Bronze Age, in the wider area of “Knossos” on the island of Crete, Greece. We instantiate two well-known spatial interaction sub-models, XTENT and Gravity, and conduct a systematic evaluation of the dynamic trading network that is formed over time. Our simulations assess the sustainability of the artificial Minoan society in terms of population size, number and distribution of agent communities, with respect to the available archaeological data and spatial interactionmodel employed; and, further, evaluate the resulting trading network’s structure (centrality, clustering, etc.) and how it a ects inter-settlement organization, providing in the process insights and support for archaeological hypotheses on the settlement organization in place at the time. Our results show that when the trading network is modeled using Gravity, which focuses on the settlements’ “importance” rather than proximity to each other, settlement numbers’ evolution patterns emerge that are similar to the ones that exist in the archaeological record. It can also be inferred by our simulations that a rather dense trading network, without a strict settlement hierarchy, could have emerged during the LateMinoan period, a er the Theran volcanic eruption, a well documented historic catastrophic event. Moreover, it appears that the trading network’s structure and interaction patterns are reversed a er the Theran eruption, when compared to those in e ect in earlier periods.
{"title":"An Agent-Based Model for Simulating Inter-Settlement Trade in Past Societies","authors":"A. Chliaoutakis, G. Chalkiadakis","doi":"10.18564/jasss.4341","DOIUrl":"https://doi.org/10.18564/jasss.4341","url":null,"abstract":"Social and computational archaeology focuses largely on the study of past societies and the evolution of human behaviour. At the same time, agent-basedmodels (ABMs) allow the e icientmodeling of human agency, and the quantitative representation and exploration of specific properties and patterns in archaeological information. In this work we put forward a novel agent-based trading model, for simulating the exchange anddistributionof resources across settlements inpast societies. Themodel is part of a broader ABMpopulated with autonomous, utility-seeking agents corresponding to households; with the ability to employ any spatial interaction model of choice. As such, it allows the study of the settlements’ trading ability and power, given their geo-location and their position within the trading network, and the structural properties of the network itself. As a case study we use the Minoan society during the Bronze Age, in the wider area of “Knossos” on the island of Crete, Greece. We instantiate two well-known spatial interaction sub-models, XTENT and Gravity, and conduct a systematic evaluation of the dynamic trading network that is formed over time. Our simulations assess the sustainability of the artificial Minoan society in terms of population size, number and distribution of agent communities, with respect to the available archaeological data and spatial interactionmodel employed; and, further, evaluate the resulting trading network’s structure (centrality, clustering, etc.) and how it a ects inter-settlement organization, providing in the process insights and support for archaeological hypotheses on the settlement organization in place at the time. Our results show that when the trading network is modeled using Gravity, which focuses on the settlements’ “importance” rather than proximity to each other, settlement numbers’ evolution patterns emerge that are similar to the ones that exist in the archaeological record. It can also be inferred by our simulations that a rather dense trading network, without a strict settlement hierarchy, could have emerged during the LateMinoan period, a er the Theran volcanic eruption, a well documented historic catastrophic event. Moreover, it appears that the trading network’s structure and interaction patterns are reversed a er the Theran eruption, when compared to those in e ect in earlier periods.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81649501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Masanori Hirano, K. Izumi, Hiroyasu Matsushima, Hiroki Sakaji
Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-tradermarket-making (HFT-MM) strategy from both the real financialmarket andour simulation. Regarding the former,weextracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the di erence between these traders’ orders and themarket price. In our simulation, we built an artificial market model with a continuous double auction system, stylized trader agents, and HFT-MM trader agents based on prior research. As an experiment, we compared the distribution of the order placements of HFT-MM traders in the real and simulated financial data. We found that the order placement distribution near the market or best price in both the real data and the simulations were similar. However, the orders far from the market or best price di ered significantly when the real data exhibited a wider range of orders. This indicates that in order to build more realistic simulation of financial markets, integrating fine-grained data is essential.
{"title":"Comparing Actual and Simulated HFT Traders' Behavior for Agent Design","authors":"Masanori Hirano, K. Izumi, Hiroyasu Matsushima, Hiroki Sakaji","doi":"10.18564/jasss.4304","DOIUrl":"https://doi.org/10.18564/jasss.4304","url":null,"abstract":"Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-tradermarket-making (HFT-MM) strategy from both the real financialmarket andour simulation. Regarding the former,weextracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the di erence between these traders’ orders and themarket price. In our simulation, we built an artificial market model with a continuous double auction system, stylized trader agents, and HFT-MM trader agents based on prior research. As an experiment, we compared the distribution of the order placements of HFT-MM traders in the real and simulated financial data. We found that the order placement distribution near the market or best price in both the real data and the simulations were similar. However, the orders far from the market or best price di ered significantly when the real data exhibited a wider range of orders. This indicates that in order to build more realistic simulation of financial markets, integrating fine-grained data is essential.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85936114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marcin Czupryna, C. Franzke, Sascha Hokamp, J. Scheffran
There is an ongoing discussion concerning the relationship between social welfare and climate change, and thus the required level and type of measures needed to protect the climate. Integrated assessment models (IAMs) have been extended to incorporate technological progress, heterogeneity and uncertainty, making use of a (stochastic) dynamic equilibrium approach in order to derive a solution. According to the literature, the IAM class of models does not take all the relationships among economic, social and environmental factors into account. Moreover, it does not consider these interdependencies at the micro-level, meaning that all possible consequences are not duly examined. Here, we propose an agent-based approach to analyse the relationship between economic welfare and climate protection. In particular, our aim is to analyse how the decisions of individual agents, allowing for the trade-off between economic welfare and climate protection, influence the aggregated emergent economic behaviour. Using this model, we estimate a damage function, with values in the order 3% - 4%for 2 C temperature increase and having a linear (or slightly concave) shape. We show that the heterogeneity of the agents, technological progress and the damage function may lead to lower GDP growth rates and greater temperature-related damage than what is forecast by models with solely homogeneous (representative) agents.
{"title":"An Agent-Based Approach to Integrated Assessment Modelling of Climate Change","authors":"Marcin Czupryna, C. Franzke, Sascha Hokamp, J. Scheffran","doi":"10.18564/jasss.4325","DOIUrl":"https://doi.org/10.18564/jasss.4325","url":null,"abstract":"There is an ongoing discussion concerning the relationship between social welfare and climate change, and thus the required level and type of measures needed to protect the climate. Integrated assessment models (IAMs) have been extended to incorporate technological progress, heterogeneity and uncertainty, making use of a (stochastic) dynamic equilibrium approach in order to derive a solution. According to the literature, the IAM class of models does not take all the relationships among economic, social and environmental factors into account. Moreover, it does not consider these interdependencies at the micro-level, meaning that all possible consequences are not duly examined. Here, we propose an agent-based approach to analyse the relationship between economic welfare and climate protection. In particular, our aim is to analyse how the decisions of individual agents, allowing for the trade-off between economic welfare and climate protection, influence the aggregated emergent economic behaviour. Using this model, we estimate a damage function, with values in the order 3% - 4%for 2 C temperature increase and having a linear (or slightly concave) shape. We show that the heterogeneity of the agents, technological progress and the damage function may lead to lower GDP growth rates and greater temperature-related damage than what is forecast by models with solely homogeneous (representative) agents.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81329214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
What drives the prices of fine wines is not easy to discern, in view of a multitude of confounding factors characterising the transactions across several markets. At the same time, understanding the quantitative relationships and mechanisms that determine the price level is important for policy making (e.g. predicting the outcomes of regulations) and methodological purposes (which elements to consider in modelling these markets). We examine the price formation of fine wines simultaneously across three markets: an automated electronic exchange (Liv-ex), intermediated auctions, andover-the-counter (OTC).Weuse auniquedataset consisting of 99,769 price data points for Premier Cru Bordeaux fine wines and we examine the price determinants with Bayesian modelling. We ascertain the mean price ranking (OTCmarket being the most expensive and Livex the least, di ering by about 4.5% and -0.8% from the auctions). We also find a slight price decrease for larger transactions (approx. 0.3% reduction for a 10% volume increase) and some platykurtosis in price distribution (greatest in Liv-ex), and observe themost stochastic noise in auctions. In an agent-based simulation, we discover that it is necessary to include trading mechanisms, commissions, and OTC market heterogeneity to reproduce the observed characteristics. Our results indicate which elements should be included in future fine wine markets models.
{"title":"Price Formation in Parallel Trading Systems: Evidence from the Fine Wine Market","authors":"Marcin Czupryna, M. Jakubczyk, Pawel Oleksy","doi":"10.18564/jasss.4349","DOIUrl":"https://doi.org/10.18564/jasss.4349","url":null,"abstract":"What drives the prices of fine wines is not easy to discern, in view of a multitude of confounding factors characterising the transactions across several markets. At the same time, understanding the quantitative relationships and mechanisms that determine the price level is important for policy making (e.g. predicting the outcomes of regulations) and methodological purposes (which elements to consider in modelling these markets). We examine the price formation of fine wines simultaneously across three markets: an automated electronic exchange (Liv-ex), intermediated auctions, andover-the-counter (OTC).Weuse auniquedataset consisting of 99,769 price data points for Premier Cru Bordeaux fine wines and we examine the price determinants with Bayesian modelling. We ascertain the mean price ranking (OTCmarket being the most expensive and Livex the least, di ering by about 4.5% and -0.8% from the auctions). We also find a slight price decrease for larger transactions (approx. 0.3% reduction for a 10% volume increase) and some platykurtosis in price distribution (greatest in Liv-ex), and observe themost stochastic noise in auctions. In an agent-based simulation, we discover that it is necessary to include trading mechanisms, commissions, and OTC market heterogeneity to reproduce the observed characteristics. Our results indicate which elements should be included in future fine wine markets models.","PeriodicalId":14675,"journal":{"name":"J. Artif. Soc. Soc. Simul.","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76441955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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