Scaling up models of decisions from experience: Information and incentives in networks
Abstract
Traditional economic theories of decision making are based on a rationality assumption of one individual (the homo-economicus). However, nearly every aspect of current societies involve interdependencies among individuals (Simon, 1957), each making decisions that are bounded by cognitive abilities and environmental constraints (Simon, 1955). Much work in decision sciences address the limitations of economic theories to improve the predictive abilities of these theories (e.g., Kahneman & Tversky, 1979). Yet these theories are often based on the assumption that full descriptive or symbolic information can be provided to the decision maker. Theories of choice under uncertainty have demonstrated robust accounting of human behavior in the absence of descriptive information, providing computational representations of theoretical principles (Erev et al., 2010). Among those, the Instance-Based Learning Theory (IBLT), a theory based on cognitive principles, is arguably the most prominent one (Glockner et al., 2016; Hertwig, 2016). IBLT is a theory of experiential-based choice that represent how a human makes decisions under uncertainty by exploration of the environment (Gonzalez, Lerch, & Lebiere, 2003; Gonzalez & Dutt, 2011). Despite its success, IBLT confronts a central challenge arising from the observation that in modem society, the welfare of individuals is increasingly dependent on the actions of others (Martin, Gonzalez, Juvina, & Lebiere, 2014). IBLT was designed to represent individual behavior, and although some efforts have been made to expand IBLT to account for social dilemmas (Martin & Gonzalez, 2011; Martin et al, 2014; Gonzalez, Ben-Asher, Martin & Dutt, 2015), it is unclear how these theories will be used to predict human behavior in complex social settings, such as those involving multi-agent networks. Research Objectives: Our goal is to expand current theories of decisions from experience to account for and explain for effects of information and incentive structures in networked groups of individuals. We will study the impact of various information and interaction formats among members To accomplish this goal we propose three key research thrusts: (1) systematic expansions of mechanisms of Instance-Based Learning Theory (IBLT) (Gonzalez et al., 2003) through the inclusion of mechanisms known in social dilemmas and, network science research; (2) empirical investigation of the interaction between information, incentives, and network structure on network efficiency and social welfare; and (3) computational implementation of cognitive models to test new theoretical expansions against experimental data. Methods: We will use a combination of theoretical developments with computational models and empirical testing in laboratory experiments. Theoretical development will involve systematic expansions of mechanisms of IBLT through the inclusion of mechanisms known in social dilemmas and network-based economic research. The empirical testing will include laboratory experiments to study the impact of information and incentive structures on network efficiency and social welfare. Innovation and significance to advancement of knowledge: Predicting the effects of information and incentive structures in social networks is critical to the welfare of society and to our ability to effectively address social problems such as vandalism, terrorism, or cybersecurity. The advancement of cognitive theories to address social challenges is also critical for training and system design of network enabled operations and command and control domains. This research will expand scientific knowledge on how to design incentive structures in networks and to present relevant information in a network structure, so that we increase the efficiency of network-based tasks.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 11, 2018
- Source ID
- W911NF1710431
Entities
People
- Cleotilde Gonzalez
Organizations
- Army Contracting Command
- Massachusetts Institute of Technology
- United States Army