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  • 1
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    Prague: Charles University in Prague, Institute of Economic Studies (IES)
    Publication Date: 2013-10-01
    Description: Kooperativní chování je nezbytnou podmínkou pro existenci moderních komplexních společenství a ekonomik, jak je známe dnes. Z pohledu společenských věd je proto zajímavé porozumnět, jak takové chování může převažovat i v situacích, kdy jednoduchá úvaha vede k opačnému očekávání, tedy že chování jedinců bude spíše charakterizované tendencí sledovat vlastní, raději než společenský zájem. Model vývoje kooperativního chování, který je prezentován v této práci, navazuje na studie, kde struktura interakcí má podobu sítě ([31], [23], [22]) a vznikl rozšířením jednoduchého modelu popsaného v [31]. Na rozdíl od původního modelu je v naší práci kladen důraz na prvek imitace a jeho vliv na rozvoj kooperativního (resp. nekooperativního) chování v simulované populaci agentů. Hlavní motivací pro nás bylo otestovat, zda evoluční dynamika vězňova dilematu hraného na komplexních sítí je skutečně ovlivňována především samotnou topologií sítě nebo je spíše souběžně determinovaná jak topologií tak dalšími evolučními mechanismy, v našem případě konkrétní podobou imitace.
    Keywords: C15 ; C73 ; D85 ; C15 ; C73 ; D85 ; ddc:330 ; kooperativní chování ; vězňovo dilema ; sítě ; simulace ; Gefangenendilemma ; Kooperation ; Simulation
    Language: Czech
    Type: doc-type:workingPaper
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  • 2
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    Prague: Charles University in Prague, Institute of Economic Studies (IES)
    Publication Date: 2013-10-01
    Description: In this article we extend the agent-based model of firms' formation and growth proposed in [4]. In [4] the firms' creation, expansion or contraction results from the interaction of heterogeneous utility maximizers. While the original model was able to replicate the power law distribution in the firms' sizes agents in the model set their utility maximizing effort levels completely freely and undetected. This led to the emergence of free riding and influenced the overall dynamics of the model. Therefore we decided to extend the original model by introducing the monitoring which is seen in the economic literature, besides for example the proper incentive scheme ([18]), as a possible way how to make employees work harder. Our motivation is to compare the extended model with both to the original case without monitoring and empirical data about firms' sizes distribution.
    Keywords: L11 ; C15 ; C16 ; ddc:330 ; monitoring ; firms' size ; power law ; agent-based model ; simulation ; heterogeneous agents ; Betriebsgröße ; Statistische Verteilung ; Agentenbasierte Modellierung ; Simulation ; Theorie
    Language: English
    Type: doc-type:workingPaper
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  • 3
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    Prague: Charles University in Prague, Institute of Economic Studies (IES)
    Publication Date: 2013-10-01
    Description: In this paper we apply agent-based methodology on an issue that is fundamental for economic prosperity and growth: the diffusion of innovations. The diffusion of innovations is one of the topics where agent-based simulation is an extremely fruitful method allowing not only the observation of stable states but also the process and development of the diffusion. Furthermore, empirical studies revealed that the topological structure of interactions among individuals importantly influences the diffusion's course and outcomes. We analyze diffusion outcomes for five different topologies, assuming markets where individuals are highly influenced by the adoption decision of their peers and innovations are introduced into the markets in two different ways: mass media campaigns and seeding procedures. Our results indicate that the topology of the relations among individuals importantly influences the speed and development of the diffusion process as well as final market penetration. Scale free topology seems to promote fast innovation diffusion, at the same time being characterized by the high uncertainty of the diffusion outcomes. Less heterogeneous networks (small worlds, two-dimensional lattice and ring) yield a much slower diffusion of the innovation, at the same time being much less unpredictable than scale free topology.
    Keywords: O31 ; O33 ; ddc:330 ; innovation diffusion ; complex networks ; scale-free networks ; Innovationsdiffusion ; Soziales Netzwerk
    Language: English
    Type: doc-type:workingPaper
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  • 4
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    Prague: Charles University in Prague, Institute of Economic Studies (IES)
    Publication Date: 2013-10-01
    Description: Using a simple computational model, we study consequences of herding behavior in population of agents connected in networks with different topologies: random networks, small-world networks and scale-free networks. Agents sequentially choose between two technologies using very simple rules based on the previous choice of their immediate neighbors. We show that different seeding of technologies can lead to very different results in the choice of majority of agents. We mainly focus on the situation where one technology is seeded randomly while the other is directed to targeted (highly connected) agents. We show that even if the initial seeding is positively biased toward the first technology (more agents start with the choice of the first technology) the dynamic of the model can result in the majority choosing the second technology under the targeted hub approach. Even if the change to majority choice is highly improbable targeted seeding can lead to more favorable results. The explanation is that targeting hubs enhances the diffusion of the firm's own technology and halts or slows-down the adoption of the concurrent one. Comparison of the results for different network topologies also leads to the conclusion that the overall results are affected by the distribution of number of connections (degree) of individual agents, mainly by its variance.
    Keywords: O33 ; C15 ; ddc:330 ; technology adoption ; simulation ; networks ; herding behavior ; Technologietransfer ; Innovationsdiffusion ; Soziales Netzwerk ; Herdenverhalten ; Simulation
    Language: English
    Type: doc-type:workingPaper
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