<p dir="ltr">The rise of digital media has allowed for unprecedented access to information. In particular, people are able to form beliefs based on information sources that span the full spectrum of reputation, information quality, and motivated biases. Such access is a double-edged sword because “with great power, comes great responsibility” (“Spider-Man”, 2002). Heterogeneity in information quality may be due to a variety of factors, and it is often up to the consumer to consider quality signals when evaluating the quality of information. My research explores this complicated process, and contributes to the understanding of how people demand and utilize information in different environments. I do so over three chapters. The first studies how people respond to signals of information quality in a sequential prediction game. In the second chapter, biased incentives are introduced in a prediction game experiment to test how intrinsic and extrinsic biases affect demand and utilization of information. The third chapter contains a survey in which subjects report their valuations of an X account that varies on political affiliation, occupation credentials, and number of followers.</p><p dir="ltr">My first chapter focuses on how subjects respond to signals of information quality. In it, subjects predict which of two urns was randomly chosen in each of 30 rounds. They observe a private ball drawn from the selected urn each round to help them make their prediction. The color of the ball signals the urn it came from. The subjects then sequentially broadcast their belief about which urn was selected for the session without revealing the color of the observed ball. Future subjects can use the previous broadcasts to infer additional information that may help them accurately predict the urn.</p><p dir="ltr">In the control, subjects exhibit very low utilization of previous predictions when informing their own behavior. While consistent with prior research, behaving in such a manner is suboptimal. To experiment on the malleability of subjects’ beliefs about the rationality of others, I implement two novel treatments. In the first, the subjects’ prediction order in the last 15 rounds is determined by their accrued earnings in the first 15 rounds, with highest earners predicting first. The prediction order is similarly determined in the second treatment, except a quiz on conditional updating ability is used. Subjects who score the highest on the quiz predict first. In both cases, the sorting mechanism is explained to the subjects.</p><p dir="ltr">Sorting on earnings yields a modest increase in valuations of previous subjects’ predictions. A much more significant increase is observed when sorting on ability. Additionally, the subjects who make the fewest irrational predictions (ones against the color of the ball when they do not have additional information to suggest otherwise) are the ones who score the best in the ability sort. Placing them at the beginning of rounds increases the entire round’s average earnings.</p><p dir="ltr">My second chapter uses a similar environment to study the role that bias plays in demanding and utilizing information. In it, participants predict which of two states (red or blue) each of 30 rounds was assigned. To aid them, participants observe two predictions from ‘experts,’ who are informed by a private signal with a known precision. Participants can bid to receive additional information about the state from two sources: a private signal and another independent expert’s prediction. Both sources’ precision is known. This method is the first of its kind, and allows for direct comparison between information types. The bid results are revealed once this process is complete. Participants then predict the state.</p><p dir="ltr">Two innovative treatments are implemented to implement bias into the basic environment exogenously. In the first, participants receive a small bonus each time they predict the state is blue. In the second, experts receive the same bonus each time they predict the state is blue instead of the participants. Surprisingly, participants value the private signal and additional expert’s prediction similarly, except when the experts are biased. This is a departure from most research using similar environments, which assume that some sub-optimal behavior can be attributed to mistrust in others’ ability to understand the environment. That assumption may warrant further and more careful evaluation. The most striking valuation behavior is when participants are biased. Their bids are higher when their existing information set already favors their bias, relative to when it is against it. Doing so is antithetical to the rational equilibrium and inconsistent with prior research on confirmation bias.</p><p dir="ltr">Participants generally utilize information obtained from a successful bid at a lower rate when it is against the initial experts than with it. No difference is detected between information sources. This is expected, albeit inconsistent with rational decision-making. One exception is noted. When participants are biased, they use the newly obtained information at a much higher rate when it is consistent with their bias than against it. Doing so is at odds with bidding behavior, as it implies participants bid more to receive information that they utilize less. Participants generally do a much better job of rationalizing and responding to the experts’ bias than their own in the experiment.</p><p dir="ltr">My third chapter is motivated partly by the findings in my first two chapters, using a more contextualized setting. In it, subjects are presented with a series of X account versions. The versions vary on political affiliation, occupation credentials, and number of followers. Subjects are asked to rate how much they would value information from each account version. Subjects value account versions with an unrevealed political party affiliation more than their analogs which report a party affiliation, regardless of the party or the subject’s beliefs.</p><p dir="ltr">A partisan penalty is uniformly implemented. Additionally, credentials are insufficient to overcome bias concerns. The penalty assessed to an account version aligning with a party is similar when the version has high credentials versus when it does not. Followers are also a valuable resource, regardless of political affiliation or credential levels. The marginal value that followers provide is similar for all account versions, meaning that even relative experts in a field should seek validation if they want to be valued by others.</p><p dir="ltr">Previous research would expect subjects to value versions more when they are congruent with their own beliefs, so these findings are surprising. Two groups are identified as the most likely to deviate and value same-typed account versions more: subjects who believe echo chambers are good and subjects who are concerned they have believed fake news in the past. The former group does not require a significant number of followers to highly value a politically congruent account version. The latter value politically unaffiliated accounts even more, but are more skeptical of opposition account versions and are even more sensitive to the number of followers they have.</p><p dir="ltr">These three chapters explore new avenues for researching how biases and expertise are evaluated and responded to. People are generally much better at considering the potential biases that others have than rationalizing their own biases. I also find good news in an era of heightened concern about eroding trust in experts. In each case, subjects respond to signals of expertise, and demonstrate efforts to exploit the information that experts provide.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26360290 |
| Date | 27 July 2024 |
| Creators | Alexander J Marchal (19201549) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Essays_in_Information_Demand_and_Utilization/26360290 |
Page generated in 0.0027 seconds