Loss aversion, the endowment effect, and gain-loss framing shape preferences for noninstrumental information

Litovsky, Y. Loewenstein, G. et al.

PNAS, Vol. 119 | No. 34

August 23, 2022

Abstract

We often talk about interacting with information as we would with a physical good (e.g., “consuming content”) and describe our attachment to personal beliefs in the same way as our attachment to personal belongings (e.g., “holding on to” or “letting go of” our beliefs). But do we in fact value information the way we do objects? The valuation of money and material goods has been extensively researched, but surprisingly few insights from this literature have been applied to the study of information valuation. This paper demonstrates that two fundamental features of how we value money and material goods embodied in Prospect Theory—loss aversion and different risk preferences for gains versus losses—also hold true for information, even when it has no material value. Study 1 establishes loss aversion for noninstrumental information by showing that people are less likely to choose a gamble when the same outcome is framed as a loss (rather than gain) of information. Study 2 shows that people exhibit the endowment effect for noninstrumental information, and so value information more, simply by virtue of “owning” it. Study 3 provides a conceptual replication of the classic “Asian Disease” gain-loss pattern of risk preferences, but with facts instead of human lives, thereby also documenting a gain-loss framing effect for noninstrumental information. These findings represent a critical step in building a theoretical analogy between information and objects, and provide a useful perspective on why we often resist changing (or losing) our beliefs.

Significance

We build on Abelson and Prentice’s conjecture that beliefs are not merely valued as guides to interacting with the world, but as cherished possessions. Extending this idea to information, we show that three key phenomena which characterize the valuation of money and material goods—loss aversion, the endowment effect, and the gain-loss framing effect—also apply to noninstrumental information. We discuss, more generally, how the analogy between noninstrumental information and material goods can help make sense of the complex ways in which people deal with the huge expansion of available information in the digital age.

From the Discussion

Economists have traditionally treated the value of information as derivative of its consequences for decision-making. While prior research on noninstrumental information has shown that this narrow view of information may be incomplete, only a few accounts have attempted to explain intrinsic preferences for information. One such account argues that people seek (or avoid) information inasmuch as doing so helps them maintain their cherished beliefs. Another proposes that people choose which information to seek or avoid by considering how it will impact their actions, affect, and cognition. Yet, outside of the curiosity literature, no existing account of information valuation considers preferences for information that has neither instrumental nor (concrete) hedonic value. By showing that key features of Prospect Theory’s value function also apply to individuals’ valuation of (even noninstrumental) information, the current paper suggests that we may also value information in some of the same fundamental ways that we value physical goods.

The research is here.

Harvey Weinstein’s ‘false memory’ defense is not backed by science

Anne DePrince & Joan Cook
The Conversation
Originally posted 10 Feb 20

Here is an excerpt:

In 1996, pioneering psychologist Jennifer Freyd introduced the concept of betrayal trauma. She made plain how forgetting, not thinking about and even mis-remembering an assault may be necessary and adaptive for some survivors. She argued that the way in which traumatic events, like sexual violence, are processed and remembered depends on how much betrayal there is. Betrayal happens when the victim depends on the abuser, such as a parent, spouse or boss. The victim has to adapt day-to-day because they are (or feel) stuck in that relationship. One way that victims can survive is by thinking or remembering less about the abuse or telling themselves it wasn’t abuse.

Since 1996, compelling scientific evidence has shown a strong relationship between amnesia and victims’ dependence on abusers. Psychologists and other scientists have also learned much about the nature of memory, including memory for traumas like sexual assault. What gets into memory and later remembered is affected by a host of factors, including characteristics of the person and the situation. For example, some individuals dissociate during or after traumatic events. Dissociation offers a way to escape the inescapable, such that people feel as if they have detached from their bodies or the environment. It is not surprising to us that dissociation is linked with incomplete memories.

Memory can also be affected by what other people do and say. For example, researchers recently looked at what happened when they told participants not to think about some words that they had just studied. Following that instruction, those who had histories of trauma suppressed more memories than their peers did.

The info is here.

Best-Ever Algorithm Found for Huge Streams of Data

Kevin Hartnett
Wired Magazine
Originally published October 29, 2017

Here is an excerpt:

Computer programs that perform these kinds of on-the-go calculations are called streaming algorithms. Because data comes at them continuously, and in such volume, they try to record the essence of what they’ve seen while strategically forgetting the rest. For more than 30 years computer scientists have worked to build a better streaming algorithm. Last fall a team of researchers invented one that is just about perfect.

“We developed a new algorithm that is simultaneously the best” on every performance dimension, said Jelani Nelson, a computer scientist at Harvard University and a co-author of the work with Kasper Green Larsen of Aarhus University in Denmark, Huy Nguyen of Northeastern University and Mikkel Thorup of the University of Copenhagen.

This best-in-class streaming algorithm works by remembering just enough of what it’s seen to tell you what it’s seen most frequently. It suggests that compromises that seemed intrinsic to the analysis of streaming data are not actually necessary. It also points the way forward to a new era of strategic forgetting.

The article is here.