In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.
Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction
Successfully navigating the social world requires reasoning
about both high-level strategic goals, such as whether to cooperate
or compete, as well as the low-level actions needed
to achieve those goals. While previous work in experimental
game theory has examined the former and work on multi-agent
systems has examined the later, there has been little work investigating
behavior in environments that require simultaneous
planning and inference across both levels. We develop a hierarchical
model of social agency that infers the intentions of other
agents, strategically decides whether to cooperate or compete
with them, and then executes either a cooperative or competitive
planning program. Learning occurs across both high-level
strategic decisions and low-level actions leading to the emergence
of social norms. We test predictions of this model in
multi-agent behavioral experiments using rich video-game like
environments. By grounding strategic behavior in a formal
model of planning, we develop abstract notions of both cooperation
and competition and shed light on the computational
nature of joint intentionality.
- Award ID(s):
- 1643413
- Publication Date:
- NSF-PAR ID:
- 10026426
- Journal Name:
- COGSCI
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
PURPOSE: Firms do not continue and prosper purely on their own individual endeavors, as each firm is influenced by the activities of others, and thus direct and indirect relationships shape the firm’s strategic management. These relationships form the tactics by which knowledge and other strategically important resources are accessed and created. Forming and maintaining ties among members of a network have been the subject of numerous research studies in the social, economic, and business literature. Our work is framed by the resource-based view of the firm perspective along with social capital theory and its shared constructs in network theory. Prior findings suggest that networking ties are strategic actions generated for firm growth and continuance. The ties may be short-term or develop into long-term relationships. The intent of this research is to fill some of the gaps in interorganizational networking strategy by analyzing five antecedents that have been suggested in the literature as individually associated with entrepreneurs’ engagement in network ties. In this way, our work provides another research avenue for examining networking’s contribution to strategic management. We hypothesized positive connections to entrepreneurs’ engagement in network ties from antecedents involving the firm’s knowledge absorptive capacity, business goals, entrepreneurial orientation, social interactions,more »
-
Abstract
Firms do not continue and prosper purely on their own individual endeavors, as each firm is influenced by the activities of others, and thus direct and indirect relationships shape the firm’s strategic management. These relationships form the tactics by which knowledge and other strategically important resources are accessed and created. Forming and maintaining ties among members of a network have been the subject for numerous research studies in the social, economic, and business literature. Our work is framed by the resource-based view of the firm perspective along with social capital theory and its shared constructs in network theory. Prior findings suggest that networking ties are strategic actions generated for firm growth and continuance. The ties may be short-term or develop into long-term relationships. The purpose of this research is to fill some of the gaps in interorganizational networking strategy by analyzing five antecedents that have been suggested in the literature as individually associated with entrepreneurs’ engagement in network ties. In this way, our work provides another research avenue for examining networking’s contribution to strategic management. We hypothesized positive connections to entrepreneurs’ engagement in network ties from antecedents involving the firm’s knowledge absorptive capacity, business goals, entrepreneurial orientation, social interactions, -
Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents’ trajectories with the planning of the robot’s trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents’ intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machinemore »
-
Thomson, R. ; Hussain, M. N. ; Dancy, C. L. ; Pyke, A. (Ed.)Recent literature has shown that racism and implicit racial biases can affect one’s actions in major ways, from the time it takes police to decide whether they shoot an armed suspect, to a decision on whether to trust a stranger. Given that race is a social/power construct, artifacts can also be racialized, and these racialized agents have also been found to be treated differently based on their perceived race. We explored whether people’s decision to cooperate with an AI agent during a task (a modified version of the Stag hunt task)is affected by the knowledge that the AI agent was trained on a population of a particular race (Black, White, or a non-racialized control condition). Thesedata show that White participants performed the best when the agent was racialized as White and not racialized at all, while Black participants achieved the highest score when the agent was racialized as Black. Qualitative data indicated that White participants were less likely to report that they believed that the AI agent was attempting to cooperate during the task and were more likely to report that they doubted the intelligence of the AI agent. This work suggests that racialization of AI agents, even if superficialmore »
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- The DOI is not currently available
