Computational Social Science for Sustainability
Foreword
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Last updated Introduction bullet points on 2024-12-17.
1. Introduction
A major challenge in promoting sustainability is to convince enough people to engage in some sustainable behavior, so that some sustainable goal (carbon-free electricity, for example) can be achieved. There exist psychological strategies for convincing people that some behavior is beneficial for an individual or an organization, such as the benefits of no-till and crop rotations in agriculture, which seem to outweigh the possible costs. Nudging and other popular ideas about promoting beneficial behaviors focus on the individual. The goal of commercial advertising is to convince people that certain products and services are valuable to buy and use. However, even if people learn perfectly what’s good and bad from others, computer models of social behavior suggest that a beneficial behavior will spread throughout a simulated population only a fraction of the time. That fraction, though, can be increased or decreased by manipulating the social networks that structure interpersonal interaction. Similarly, social network structure and the randomness of social interaction leads us to conclude that polarization is not inevitable in a group, given some initial set of group opinions.
To learn these lessons and to improve rigor in social and behavioral science, one must create models of interpersonal interaction. In computational models that we will study, social phenomena really emerge from simulated interpersonal interactions. A model is a simplified description of real-world phenomena. A computational model just uses a computer to solve equations, simulate, or statistically analyze some _phenomenon of interest. Social science models are most useful with a minimal number of empirically-motivated assumptions that still generate emergent social phenomena. The emergent social phenomena here are innovation diffusion, opinion and norm change, and widespread economic cooperation. These phenomena are emergent because they emerge from a series interactions between people, sometimes repeatedly between the same people, over time.
One of the learning goals of this course is to develop a theoretical understanding of how social phenomena emerge from social interactions over time. Social science theory is important because it identifies basic cognitive and social processes and behaviors and how they produce emergent social phenomena of interest (Kauffman 1970; Craver 2006). Interpersonal interaction can take several forms, but we focus on three categories: (1) social learning, (2) social influence, and (3) cooperation. Social networks constrain who interacts with whom in the real world, and so too in our computational models.
Social networks
Social networks are fundamental to modeling how behavior spreads in populations. As we will see ourselves in the next section, even if people learn perfectly well from one another with no social friction, they are not guaranteed to spread to every person in a simulated population.