Cellular automata for the spreading of technologies in socio-economic systems

Abstract

We introduce an agent-based model for the spreading of technological developments in socio-economic systems where the technology is mainly used for the collaboration/interaction of agents. Agents use products of different technologies to collaborate with each other which induce costs proportional to the difference of technological levels. Additional costs arise when technologies of different providers are used. Agents can adopt technologies and providers of their interacting partners in order to reduce their costs leading to microscopic rearrangements of the system. Analytical calculations and computer simulations revealed that starting from a random configuration of different technological levels a complex time evolution emerges where the spreading of advanced technologies and the overall technological progress of the system are determined by the amount of advantages more advanced technologies provide, and by the structure of the social environment of agents. We show that agents tend to form clusters of identical technological level with a power law size distribution. When technological progress arises, the spreading of technologies in the system can be described by extreme order statistics.

Opinion evolution influenced by informed agents

“…6 In terms of the possible values of opinions, the opinion dynamics models can be categorized into three main groups: 7 (1) discrete opinion models; (2) continuous opinion models; (3) continuous opinions and discrete actions (CODA) model [12]. 8 These opinion dynamics models, although are not able to precisely predict the real-world observations in some cases, can 9 help us capture the critical factors that govern the opinion evolution and diffusion. 10 In the continuous opinion models, the opinion of each agent ranges within a continuous opinion space (e.g., [0, 1]).…”

“…Among several different discrete opinion 8 models, we highlight voter model [17,18], Sznajd model [19][20][21], and majority-rule model [22][23][24]. In the voter model, each 9 agent is randomly chosen to adopt the opinion of one of his neighbors, whereas in the Sznajd model two agents with the Q3 10 same opinion persuade their neighbors to accept their opinion. The agents in the majority-rule model update their opinions 11 by following the local majority rule.…”

Competition of information channels in the spreading of innovations

“…(2) Spreading-Successful technologies spread over the system, resulting in an overall technological progress. The understanding of the technological progress of socioeconomic systems requires the analysis of the complex dynamics of several levels from the interaction of firms investing in research and doing production [5][6][7], through the emergence of new ideas captured by the network of patents [8], down to the level of individuals that use the newly developed products [9][10][11][12]. A crucial element of the diffusion of technological advancements is the spreading of information about the existence and advantages of the newly developed technology.…”

“…Diffusion of innovations also involves * feri@dtp.atomki.hu components analogous to opinion spreading and consensus formation [9][10][11][12][16][17][18][19][20][21][22][23][24][25][26][27]. As an example, telecommunication technologies can be mentioned, where the usage of a technology assumes that the communication partners are approximately at the same technological level [1,[9][10][11][12]19,20].…”

Mathematical approaches for complexity/predictivity trade-offs in complex system models : LDRD final report.

“…It has even been suggested that cellular automata and related systems are candidates for expressing fundamental laws of nature, in contrast to traditional mathematical approaches based on differential equations [34]. At a different level, the relevance of such idealized systems to real-world applications is illustrated by a cellular-automaton model for the adoption of a new technology in an economy [24].…”