A percolation-based model explaining delayed takeoff in new-product diffusion

Hohnisch, Martin; Pittnauer, Sabine; Stauffer, Dietrich

doi:10.1093/icc/dtn031

Cited by 47 publications

(35 citation statements)

References 25 publications

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“…As opposed to physical systems, where the interactions are usually individual↔individual in social and economic systems, the individual→collective and collective→individual effects are significant , Hohnisch et al 2008, Cantono and Silverberg 2009.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the autocatalytic loop selects a limited number of parameters as relevant to the macroscopic behavior of the system. We have chosen as an illustration of this mechanism the social/market percolation framework, as presented in recent works (Antonelli 1996, Goldenberg et al 2000, Bornholdt 2001, Aleksiejuk and Holyst 2001, Weisbuch et al 2001, Erez et al 2005, Goldenberg et al 2005, Yaari et al 2006, Erez et al 2007, Frenken et al 2008, Hohnisch et al 2008, Sieczka and Holyst 2009, Lorenz et al 2009. We consider here the example of the economic crisis and bankruptcy contagion.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, as opposed to physical systems where the dynamics are usually individual↔individual, in social and economic systems there are significant collective→individual and individual→collective effects, such as government interventions, the general state of the economy, the 'mood of the 3 market' and mass media. Such effects have been shown in the past to lead to self-organized percolation criticality , different stages of technological development along the product lifecycle (Frenken et al 2008), long tails at the early stages of innovation diffusion (Hohnisch et al 2008) and dependence of demand-pull policies on the learning curve (Cantono and Silverberg 2009).…”

Section: Introductionmentioning

confidence: 99%

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When the collective acts on its components: economic crisis autocatalytic percolation

Cantono

Solomon

2010

New J. Phys.

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Agent-based models have improved the standards for empirical support and validation criteria in social, biological, cognitive and human sciences. Yet, the inclusion, in these models, of vertical interactions between various aggregation levels remains a challenge. We study analytically, numerically and by simulation the generic consequences of interactions between the collective and its individual components: the appearance of an autocatalytic loop between the dynamics of the collective and its components; the system, which is dominated by a limited number of factors amplified by this collective↔individuals autocatalytic loop; the microscopic features, which are not involved in the autocatalytic loop and are irrelevant at the systemic level; and how the above clarify the interplay between macroscopic predictable features and the ones dependent on random unpredictable individual events. Using the social and market percolation framework, we study the dramatic effects of the collective↔individuals autocatalytic loop on economic crisis propagation: the percolation transition becomes discontinuous; there are a few relevant regions and regimes corresponding to a quite diverse range of response policy options; there are stability ranges where appropriate policies can help to avoid macroscopic crisis percolation; and beyond those regions the systemic crisis might become unstoppable.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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When the collective acts on its components: economic crisis autocatalytic percolation

Cantono

Solomon

2010

New J. Phys.

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“…Drawing on recent percolation models [6,7,8,9] of diffusion, which combine the contagion aspect (e.g., epidemic models) with the heterogeneity of agent characteristics (e.g.,…”

Section: Introductionmentioning

confidence: 99%

“…According to Stauffer ). Percolation theory has been applied to social science [7] as well as to the economics of technology diffusion [8,9].…”

Section: Extending Standard Models Of Diffusion By Introducing Pementioning

confidence: 99%

A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies

Cantono

Silverberg

2009

Technological Forecasting and Social Change

144

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An obstacle to the widespread adoption of environmentally friendly energy technologies such as stationary and mobile fuel cells is their high upfront costs. While much lower prices seem to be attainable in the future due to learning curve cost reductions that increase rapidly with the scale of diffusion of the technology, there is a chicken and egg problem, even when some consumers may be willing to pay more for green technologies. Drawing on recent percolation models of diffusion by Solomon et al. [7], Frenken et al. [8] and Höhnisch et al.[9], we develop a network model of new technology diffusion that combines contagion among consumers with heterogeneity of agent characteristics. Agents adopt when the price falls below their random reservation price drawn from a lognormal distribution, but only when one of their neighbors has already adopted. Combining with a learning curve for the price as a function of the cumulative number of adopters, this may lead to delayed adoption for a certain range of initial conditions. Using agent-based simulations we explore when a limited subsidy policy can trigger diffusion that would otherwise not happen. The introduction of a subsidy policy seems to be highly effective for a given high initial price level only for learning economies in a certain range. Outside this range, the diffusion of a new technology either never takes off despite the subsidies, or the subsidies are unnecessary. Perhaps not coincidentally, this range seems to correspond to the values observed for many successful innovations.JEL Codes: C61, H23, O32, O33

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Simulating the influence of Facebook fan pages on individual attitudes toward vaccination using agent‐based modelling

2022

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The anti‐vaccination movement is dangerous because of its influence on vaccine hesitancy. Nowadays, social media platforms become significant sources of anti‐vaccination information; therefore, combating their proliferation needs to be addressed by the relevant authorities. Previous studies suggested two policies to mitigate the negative influence of anti‐vaccination information online: attaching caution banners from healthcare authorities and engaging in censorship of anti‐vaccine supporting information providers. However, these recommendations were obtained without considering how the users form their sentiments. In this paper, we explore the influence of the existing network of vaccination‐related Facebook pages on an individual user's vaccination sentiment using agent‐based modelling (ABM). We use the ABM implementation of the Zaller model to convert the user's information consumption to their vaccination sentiment. Our simulation results show that the application of the two policies leads to improved sentiment on vaccination, reinforcing existing suggestions obtained by different methods.

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A percolation-based model explaining delayed takeoff in new-product diffusion

Cited by 47 publications

References 25 publications

When the collective acts on its components: economic crisis autocatalytic percolation

When the collective acts on its components: economic crisis autocatalytic percolation

A percolation model of eco-innovation diffusion: The relationship between diffusion, learning economies and subsidies

Simulating the influence of Facebook fan pages on individual attitudes toward vaccination using agent‐based modelling

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