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A growing body of research uses computational models to study political decision making and behavior such as voter turnout, vote choice, party competition, social networks, and cooperation in social dilemmas. Advances in the computational modeling of political decision making are closely related to the idea of bounded rationality. In effect, models of full rationality can usually be analyzed by hand, but models of bounded rationality are complex and require computer-assisted analysis. Most computational models used in the literature are agent based, that is, they specify how decisions are made by autonomous, interacting computational objects called “agents.” However, an important distinction can be made between two classes of models based on the approaches they take: behavioral and information processing. Behavioral models specify relatively simple behavioral rules to relax the standard rationality assumption and investigate the system-level consequences of these rules in conjunction with deductive, game-theoretic analysis. In contrast, information-processing models specify the underlying information processes of decision making—the way political actors receive, store, retrieve, and use information to make judgment and choice—within the structural constraints on human cognition, and examine whether and how these processes produce the observed behavior in question at the individual or aggregate level. Compared to behavioral models, information-processing computational models are relatively rare, new to political scientists, and underexplored. However, focusing on the underlying mental processes of decision making that must occur within the structural constraints on human cognition, they have the potential to provide a more general, psychologically realistic account for political decision making and behavior.

Article

Citizens are continuously inundated with political information. How do citizens process that information for use in decision-making? Political psychologists have generally thought of information processing as proceeding through a series of stages: (1) exposure and attention; (2) comprehension; (3) encoding, interpretation, and elaboration; (4) organization and storage in memory; and (5) retrieval. This processing of information relies heavily on two key structures: working memory and long-term memory. Working memory actively processes incoming information whereas long-term memory is the storage structure of the brain. The most widely accepted organizational scheme for long-term memory is the associative network model. In this model, information stored in long-term memory is organized as a series of connected nodes. Each node in the network represents a concept with links connecting the various concepts. The links between nodes represent beliefs about the connection between concepts. These links facilitate retrieval of information through a process known as spreading activation. Spreading activation moves information from long-term memory to working memory. When cued nodes are retrieved from memory, they activate linked nodes thereby weakly activating further nodes and so forth. Repeatedly activated nodes are the most likely to be retrieved from long-term memory for use in political decision-making. The concept of an associative network model of memory has informed a variety of research avenues, but several areas of inquiry remain underdeveloped. Specifically, many researchers rely on an associative network model of memory without questioning the assumptions and implications of the model. Doing so might further inform our understanding of information processing in the political arena. Further, voters are continuously flooded with political and non-political information; thus, exploring the role that the larger information environment can play in information processing is likely to be a fruitful path for future inquiry. Finally, little attention has been devoted to the various ways a digital information environment alters the way citizens process political information. In particular, the instantaneous and social nature of digital information may short-circuit information processing.