An official website of the United States government
While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g. learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g. with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan “All US Adults,” or partisan “Democrat” / “Republican”) and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further.
more »
« less
Incomplete Preferences and Rational Framing Effects
The normative principle of description invariance presupposes that rational preferences must be complete. The completeness axiom is normatively dubious, however, and its rejection opens the door to rational framing effects. In this commentary, we suggest that Bermúdez’s insightful challenge to the standard normative view of framing can be clarified and extended by situating it within a broader critique of completeness.
more »
« less
- Award ID(s):
- 2049935
- PAR ID:
- 10481521
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Journal Name:
- Behavioral and brain sciences
- ISSN:
- 1469-1825
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The adoption of carbon dioxide removal (CDR) technologies at a scale sufficient to draw down carbon emissions will require both individual and collective decisions that happen over time in different locations to enable a massive scale-up. Members of the public and other decision-makers have not yet formed strong attitudes, beliefs and preferences about most of the individual CDR technologies or taken positions on policy mechanisms and tax-payer support for CDR. Much of the current discourse among scientists, policy analysts and policy-makers about CDR implicitly assumes that decision-makers will exhibit unbiased, rational behaviour that weighs the costs and benefits of CDR. In this paper, we review behavioural decision theory and discuss how public reactions to CDR will be different from and more complex than that implied by rational choice theory. Given that people do not form attitudes and opinions in a vacuum, we outline how fundamental social normative principles shape important intergroup, intragroup and social network processes that influence support for or opposition to CDR technologies. We also point to key insights that may help stakeholders craft public outreach strategies that anticipate the nuances of how people evaluate the risks and benefits of CDR approaches. Finally, we outline critical research questions to understand the behavioural components of CDR to plan for an emerging public response.more » « less
-
null (Ed.)We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive Bayesian agent which solves a contextual bandit problem. Our methodology integrates three key components. First, to identify distinct commenting patterns, we use deep embedded clustering to estimate framing information (essential extrinsic features) that clusters users into distinct groups. Second, we present an inverse reinforcement learning algorithm that uses Bayesian revealed preferences to test for rationality: does there exist a utility function that rationalizes the given data, and if yes, can it be used to predict commenting behavior? Finally, we impose behavioral economics constraints stemming from rational inattention to characterize the attention span of groups of users. The test imposes a Rényi mutual information cost constraint which impacts how the agent can select attention strategies to maximize their expected utility. After a careful analysis of a massive YouTube dataset, our surprising result is that in most YouTube user groups, the commenting behavior is consistent with optimizing a Bayesian utility with rationally inattentive constraints. The paper also highlights how the rational inattention model can accurately predict commenting behavior. The massive YouTube dataset and analysis used in this paper are available on GitHub and completely reproduciblemore » « less
-
https://peer.asee.org/27950 This paper presents results of work completed on our project, Intersectionality of Non-normative Identities in the Cultures of Engineering (InIce). The overarching focus of this project is on how students who hold non-normative identities position themselves, grow through their education, and navigate the cultures of engineering they experience in college. Our goal is to investigate ways to engage students who hold non-normative identities to become more active and lifelong participants in engineering disciplines. Our work is proceeding in three phases: 1) Identify, through a quantitative instrument, the attitudinal profiles of normative and non-normative students in engineering; 2) Characterize students’ normative and non-normative identities through in-depth interviews and analysis of differences between students with normative and non-normative identities in engineering; and 3) Drawing from our findings, develop a workshop and set of courses to incorporate diversity topics into engineering programs to enhance the culture of engineering to be more responsive towards, and inclusive of, a diverse range of student identities. We have completed the first phase of the project in which we quantitatively measured and characterized student groups with normative and non-normative identities in engineering. Our definitions of normative and non-normative for this project are developed through Topological Data Analysis (TDA) of a set of multi-institution survey data (n = 2916). TDA allows identification of groups without imposing a priori hypotheses on how the attitudes of students may group together (nor how they may distinguish between demographic groups). This approach allows the underlying structure of the data to emerge rather than imposing pre-defined definitions of normative attitudes or identities. Our TDA results revealed one group that contains a relatively large number of students (the “normative” group) and a total of seven other distinct, but relatively populated, groups (the “non-normative” groups). We have compiled a summary of the most salient attitudinal constructs in terms of characterizing and distinguishing between all these groups including: motivation (value, goal orientation, future time perspective), engineering and physics identities (performance/competence and recognition beliefs for each), personality traits (neuroticism, extraversion, belongingness) and grit (consistency of interest). We are currently in Phase 2 of our study in which we are conducting a series of qualitative, longitudinal interviews with students selected from normative and non-normative groups to understand how they navigate their engineering experiences and define their educational trajectories over the first two years of college. This data will be deductively analyzed based on our existing attitudinal frameworks as well as inductively coded for emerging themes on how students feel belongingness within engineering culture. This project promises to move traditional measures of demographic data beyond socially constructed perceptions of others and allows for the representation of student diversity from the perspective of each participant. This more accurate reflection of diversity provides novel insight into the experiences of students who might otherwise be ignored or unjustifiably lumped in with other students with whom they share some demographic indicator and how residing at the intersection of multiple measures of diversity can influence students’ experiences in engineering culture.more » « less
-
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithm to be “fair.” Most of these propositions are inspired by, but inadequately grounded in, scholarship from political philosophy. This comic aims to correct that deficit. We begin by setting up a working definition of an 'Automated Decision System' (ADS) and explaining 'bias' in outputs of an ADS. We then critically evaluate different definitions of fairness as Equality of Opportunity (EOP) by contrasting their conception in political philosophy (such as Rawls’s fair EOP and formal EOP) with the proposed codification in Fair-ML (such as statistical parity, equality of odds and accuracy) to provide a clearer lens with which to view existing results and to identify future research directions. We use this framing to reinterpret the impossibility results as the incompatibility between different EOP doctrines and demonstrate how political philosophy can provide normative guidance as to which notion of fairness is applicable in which context. We conclude by highlighting justice considerations that the fair-ML literature currently overlooks or underemphasizes, such as Rawls's broader theory of justice, which supplements his EOP principle with a principle guaranteeing equal rights and liberties to all citizens in a free and democratic society.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
