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3 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/
This model aims to simulate Competition and Displacement of Online Interpersonal Communication Platforms process from a bottom-up angle. Individual interpersonal communication platform adoption and abandonment serve as the micro-foundation of the simulation model. The evolution mode of platform user online communication network determines how present platform users adjust their communication relationships as well as how new users join that network. This evolution mode together with innovations proposed by individual interpersonal communication platforms would also have impacts on the platform competition and displacement process and result by influencing individual platform adoption and abandonment behaviors. Three scenes were designed to simulate some common competition situations occurred in the past and current time, that two homogeneous interpersonal communication platforms competed with each other when this kind of platforms first came into the public eye, that a late entrant platform with a major innovation competed with the leading incumbent platform during the following days, as well as that both the leading incumbent and the late entrant continued to propose many small innovations to compete in recent days, respectively.
Initial parameters are as follows: n(Nmax in the paper), denotes the final node number of the online communication network node. mi (m in the paper), denotes the initial degree of those initial network nodes and new added nodes. pc(Pc in the paper), denotes the proportion of links to be removed and added in each epoch. pst(Pv in the paper), denotes the proportion of nodes with a viscosity to some platforms. comeintime(Ti in the paper), denotes the epoch when Platform 2 joins the market. pit(Pi in the paper), denotes the proportion of nodes adopting Platform 2 immediately at epoch comeintime(Ti). ct(Ct in the paper), denotes the Innovation Effective Period length. In Scene 2, There is only one major platform proposed by Platform 2, and ct describes that length. However, in Scene 3, Platform 2 and 1 will propose innovations alternately. And so, we set ct=10000 in simulation program, and every jtt epochs, we alter the innovation proposer from one platform to the other. Hence in this scene, jtt actually denotes the Innovation Effective Period length instead of ct.
The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure
I added a discounting rate to the equation for expected values of defective / collaborative strategies.
The discounting rate was set to 0.956, the annual average from 1980 to 2015, using the Consumer Price Index (CPI) of Statistics Korea.
The model that simulates the dynamic creation and maintenance of knowledge-based formations such as communities of scientists, fashion movements, and subcultures. The model’s environment is a spatial one, representing not geographical space, but a “knowledge space” in which each point is a different collection of knowledge elements. Agents moving through this space represent people’s differing and changing knowledge and beliefs. The agents have only very simple behaviors: If they are “lonely,” that is, far from a local concentration of agents, they move toward the crowd; if they are crowded, they move away.
Running the model shows that the initial uniform random distribution of agents separates into “clumps,” in which some agents are central and others are distributed around them. The central agents are crowded, and so move. In doing so, they shift the centroid of the clump slightly and may make other agents either crowded or lonely, and they too will move. Thus, the clump of agents, although remaining together for long durations (as measured in time steps), drifts across the view. Lonely agents move toward the clump, sometimes joining it and sometimes continuing to trail behind it. The clumps never merge.
The model is written in NetLogo (v6). It is used as a demonstration of agent-based modelling in Gilbert, N. (2008) Agent-Based Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc. and described in detail in Gilbert, N. (2007) “A generic model of collectivities,” Cybernetics and Systems. European Meeting on Cybernetic Science and Systems Research, 38(7), pp. 695–706.
We establish a double-layer network for China’s financial system, consisting of an interbank lending network and a cross-shareholding network. The loss of diffusion in an interbank lending channel independently, a cross-shareholding channel independently and a double-layer contagion channel after one of the financial institutions goes bankrupt with an initial shock are simulated to explore the nonlinear evolution mechanism of financial risk and impact factors of financial systemic risk in China.
The model aims at estimating household energy consumption and the related greenhouse gas (GHG) emissions reduction based on the behavior of the individual household under different operationalizations of the Theory of Planned Behaviour (TPB).
The original model is developed as a tool to explore households decisions regarding solar panel investments and cumulative consequences of these individual choices (i.e. diffusion of PVs, regional emissions savings, monetary savings). We extend the model to explore a methodological question regarding an interpretation of qualitative concepts from social science theories, specifically Theory of Planned Behaviour in a formal code of quantitative agent-based models (ABMs). We develop 3 versions of the model: one TPB-based ABM designed by the authors and two alternatives inspired by the TPB-ABM of Schwarz and Ernst (2009) and the TPB-ABM of Rai and Robinson (2015). The model is implemented in NetLogo.
This model introduces individual bias to the model of exploration and exploitation, simulates knowledge diffusion within organizations, aiming to investigate the effect of individual bias and other related factors on organizational objectivity.