La adopción tecnológica: entre dos paradigmas de simulación

Technological adoption: between two simulation paradigms

Julián Alberto Uribe Gómez

Technological adoption is perhaps one of the best known and widely researched social phenomena in the academic world, for the study of cycles of acceptance and dissemination of new products or services. A known example is the diffusion model of Bass, which seeks to study the behavior between innovators and imitators. This model was developed in 1969 [1] and to date it is still valid and widely applied for the empirical study of the diffusion models of new technologies and innovations in marketing, strategy, technological administration, among others [2].

The principle by which Bass's model is governed is based on the existence of a system with two possible states: potential adopters and adopters, where the group of potential adopters pass to the group of adopters when they acquire through the purchase or use of a product or innovative service technology for the first time [3].

The Bass model uses three main parameters: potential market (potential adopters), contact coefficient, and adoption coefficient. This has led to mathematical formulations that come from physics and biology to model the phenomenon as follows [3]:

Analytically, the model offers a predictive panorama of the behavior when generating numerical outputs of the phenomenon, likewise, with this the variables and their relationships can be represented, but it is necessary to repeat the solution several times to be able to trace the trajectories of the adoption phenomenon. However, equational models have found support in computational and simulation models to represent phenomena, not only analytically but also descriptively, especially under two paradigms: system dynamics and agent-based models.

Each simulation paradigm provides support to understand the behavior of social and technological phenomena from two perspectives: a strategic and a tactical one. In this way, these methodologies can be found that help the analyst to model these situations according to his research, academic or professional need.

To model the diffusion phenomenon of Bass through system dynamics, the causal diagrams are used to represent the multiple relationships between variables. To simulate their descriptive behavior, flow and level diagrams are used. With the objective of creating the model, the web platform https://insightmaker.com/ was used, which is an open access platform that allows you to quickly create models under the system dynamics paradigm, which can be seen in Figure 1.

To represent the adoption model through the agent-based simulation paradigm, unlike system dynamics, several aspects need to be taken into account:

1. Generate rules of behavior to the entities to simulate to obtain macro behaviors.

2. Define the interaction environment of the agents.

3. Schedule the simulation and its states.

In this case, the NETLOGO platform https://ccl.northwestern.edu/netlogo/ was used for modeling, which is free to use and designed for the study of this paradigm and was used to generate the proposed model, which was You can see in figure 2.

Simulating technological adoption through the two built models can show similar behaviors in their results, however, the difference between the behaviors is due to the fact that the agent-based simulation models are mainly of discrete order, while the dynamic models of systems are continuous. As can be seen in Figures 3 and 4, both generate the well-known "S" curves of Bass's analytical model, giving the analytical theory the descriptive component of their behavior.

Now, both simulation paradigms turn out to be descriptors of the phenomenon, however, in agent-based modeling the interaction of agents during the phenomenon can be observed, as can be seen in Figure 5, and manipulate the environment in which agents find to build various adoption scenarios.

In conclusion:

Simulation paradigms can be very useful tools when describing and studying the behavior of phenomena, which are sometimes complex. These paradigms have several levels of abstraction to represent situations according to the need for analysis, thus, in system dynamics it is necessary to understand at a macro, strategic and high level of abstraction, while agent-based modeling does not only support a high level of abstraction, but you can also interact directly with the entities playing with their rules of behavior and with multiple scenarios.