This is the third and final post looking at William Davies book Nervous States–Democracy and the Decline of Reason. Davies provides some ideas for getting out of this mess at the end of the book. I believe that they are well thought out. First, Davies notes that there is one problem confronting humanity that may never go away, and which computers do nothing to alleviate: how to make promises. A promise made to a child or a public audience has a binding power. It can be broken, but the breaking of it is a breach that can leave deep emotional and cultural wounds. Davies states:
“Whether we like it or not, the starting point for this venture will be the same as it was for Hobbes: the modern state, issuing laws backed by sovereign power. It is difficult to conceive how promises can be made at scale, in a complex modern society, without the use of contracts, rights and statutes underpinned by sovereign law. Only law really has the ability to push back against the rapidly rising tide of digital algorithmic power. It remains possible to make legal demands on the owners and controllers of machines, regardless of how sophisticated those machines are.”
This book, Nervous States – Democracy and the Decline of Reason, 2019, written by William Davies tries to explain the state we are in. The end of truth or the domination of feelings or the end of expertise all come to mind. People perceive that change is so fast that the slow knowledge developed by reason and learning is devalued, while instant knowledge that will be worthless tomorrow like that used by commodity, bond or stock trading networks is highly valued. Davies builds on Hayek and says many things that ring true. In three posts, I will present the main points of Davies’ book, argue with some of the points, and present what Davies says we can do about it. Devaluing reason is a big deal for decision making.
This post is derived from a review article: “The Role of Intuition in the Generation and Evaluation Stages of Creativity,” authored by Judit Pétervári, Magda Osman and Joydeep Bhattacharya that appeared in Frontiers of Psychology, September 2016 doi: 10.3389/fpsyg.2016.01420. It struck me that in all this blog’s posts, creativity had almost never come up. Then I threw it together with Edward O Wilson’s 2017 book: The Origins of Creativity, Liveright Publishing, New York. (See posts Evolution for Everyone and Cultural Evolution for more from Edward O. Wilson. He is the ant guy. He is interesting, understandable, and forthright.)
Creativity is notoriously difficult to capture by a single definition. Petervari et al suggest that creativity is a process that is broadly similar to problem solving, in which, for both, information is coordinated toward reaching a specific goal, and the information is organized in a novel, unexpected way. Problems which require creative solutions are ill-defined, primarily because there are multiple hypothetical solutions that would satisfy the goals. Wilson sees creativity beyond typical problem solving.
This post is largely a continuation of the Kenneth R Hammond post, but one prompted by recent current events. My opinion on gun control is probably readily apparent. But if it is not, let me say that I go crazy when mental health is bandied about as the reason for our school shootings or when we hear that arming teachers is a solution to anything. However, going crazy or questioning the sincerity of people with whom you are arguing is not a good idea. Dan Kahan (See my posts Cultural Cognition or Curiosity or his blog Cultural Cognition) has some great ideas on this, but Ken Hammond actually had accomplishments and they could help guide all of us today. I should note also that I was unable to quickly find the original sources so I am relying completely on: “Kenneth R. Hammond’s contributions to the study of judgment and decision making,” written by Mandeep K. Dhami and Jeryl L. Mumpower that appeared in Judgment and Decision Making, Vol. 13, No. 1, January 2018, pp. 1–22.
This post is based on a comment paper: “Honest People Tend to Use Less–Not More—Profanity: Comment on Feldman et al.’s (2017) Study,” that appeared in Social Psychological and Personality Science 1-5 and was written by R. E. de Vries, B. E. Hilbig, Ingo Zettler, P. D. Dunlop, D. Holtrop, K. Lee, and M. C. Ashton. Why would honesty suddenly be important with respect to decision making when I have largely ignored it in the past? You will have to figure that out for yourself. It reminded me that most of our decision making machinery is based on relative differences. We compare, but we are not so good at absolutes. Thus, when you get a relentless fearless liar, the relative differences are widened and this is likely to spread out what seems to be a reasonable decision.
This post is based on the paper: “The Neural Basis of Risky Choice with Affective Outcomes,” written by Renata S. Suter, Thorsten Pachur, Ralph Hertwig, Tor Endestad, and Guido Biele
that appeared in PLOS ONE journal.pone.0122475 April 1, 2015. The paper is similar to one discussed in the post Affect Gap that included Pachur and Hertwig although that paper did not use fMRI. Suter et al note that both normative and many descriptive theories of decision making under risk have typically investigated choices involving relatively affect-poor, monetary outcomes. This paper compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems.
The paper is notable in that it not only examined behavioral differences between affect rich and affect poor risky choice, but also watched the brains of the people making the decisions with fMRI. The researchers assert that the traditional notion of a mechanism that assumes sensitivity to outcome and probability information and expectation maximization may not hold when options elicit relatively high levels of affect. Instead, qualitatively different strategies may be used in affect-rich versus affect-poor decisions. This is not much of a leap.
In order to examine the neural underpinnings of cognitive processing in affect-rich and affect poor decisions, the researchers asked participants to make choices between two options with relatively affect-rich outcomes (drugs that cause a side effect with some probability) as well as between two options with relatively affect-poor outcomes (lotteries that incur monetary losses with some probability). The monetary losses were matched to each individual’s subjective monetary evaluation of the side effects, permitting a within-subject comparison between affect-rich and affect-poor choices in otherwise monetarily equivalent problems. This was cleverly done. Specifically, participants were first asked to indicate the amount of money they considered equivalent to specific nonmonetary outcomes (here: side effects; Fig 1A). The monetary amounts indicated (willingness-to-pay; WTP) were then used to construct individualized lotteries in which either a side effect (affect-rich problem) or a monetary loss (affect-poor problem) occurred with some probability. For example, consider a participant who specified a WTP of $18 to avoid insomnia and $50 to avoid depression. In the affect-rich problem, she would be presented with a choice between drug A, leading to insomnia
with a probability of 15% (no side effects otherwise), and drug B, leading to depression
with a probability of 5% (no side effects otherwise). In the corresponding affect-poor problem,
she would be presented with a choice between lottery A, leading to a loss of $18 with a probability of 15% (nothing otherwise), and lottery B, leading to a loss of $50 with a probability of 5% (nothing otherwise). This paradigm allowed the authors to compare the decision mechanisms underlying affect-rich versus affect-poor risky choice on the basis of lottery problems that were equivalent in monetary terms (Fig 1A and 1B).
To assess whether experiencing a side effect was rated as evoking stronger negative affect than losing the equivalent monetary amount, Suter et al analyzed the ratings. Fig 2. presents them.
Were the differences in affect associated with different choices? Their findings showed that, despite the monetary equivalence between affect-rich and affect-poor problems, people reversed their preferences between the corresponding problems in 46.07% of cases, on average. To examine the cognitive mechanisms underlying affect-rich and affect-poor choices, the researchers modeled them using Cumulative Prospect Theory (CPT). On average, CPT based on individually fitted parameters correctly described participants’ choices in 82.45% of affect-rich choices and in 90.42% of affect-poor choices. Modeling individuals’ choices using CPT, they found affect-rich choice was best described by a substantially more strongly curved weighting function than affect-poor choice, signaling that the psychological impact of probability information is diminished in the context of emotionally laden outcomes. Participants seemed to avoid the option associated with the worse side effects, irrespective of their probabilities, and therefore often ended up choosing the option with the lower expected value.
The neural testing was complicated and used extensive computational modeling analysis. Neuroimaging analyses further supported the hypothesis that choices between affect-rich options are based on qualitatively different cognitive processes than choices between affect poor options; the two triggered qualitatively different brain circuits. Affect-rich problems engage more affective processing, as indicated by stronger activation in the amygdala. The results suggested that affect-poor choice is based on calculative processes, whereas affect-rich choice involves emotional processing and autobiographical memories. When a choice elicits strong emotions, decision makers seem to focus instead on the potential outcomes and the memories attached to them.
According to Suter et al, on a theoretical level, models assuming expectation maximization (and implementing the weighting of some function of outcome by some function of probability) may fail to accurately predict people’s choices in the context of emotionally laden outcomes. Instead, alternative modeling frameworks (e.g., simplifying, lexicographic cognitive strategies) may be more appropriate. On a practical level, the researchers suggest that to the extent that people show strongly attenuated sensitivity to probability information (or even neglect it altogether) in decisions with affect-rich outcomes, different decision aids may be required to help them make good choices. For instance, professionals who communicate risks, such as doctors or policy makers, may need to pay special attention to refocusing people’s attention on the probabilities of (health) risks by illustrating those risks visually.
This paper does not present things in ways that I have seen often. It focuses on the most compensatory analytic strategies like prospect theory and says that these strategies do not reflect how we make decisions that are emotionally laden. It suggests that simplifying lexicographic strategies may be more appropriate. Other studies that have used decision times and eye tracking instead of fMRI have made it clear that compensatory analytic strategies do not reflect actual decision making, although not as definitively. We also know it from our own experiences. However, from my understanding, this does not necessarily push us to lexicographic strategies. There are compensatory strategies like parallel constraint satisfaction that might also be the explanation. It may be that this is just part of the cognitive niches v. parallel constraint satisfaction or evidence accumulation decision models debate. Fuzzy trace theory is another candidate that is not a lexicographic strategy.
This post is based on a doctoral dissertation: “Just do it! Guilt as a moral intuition to cooperate–A parallel constraint satisfaction approach,” written by Thomas Stemmler at the University of Wurzburg. Stemmler does a good job of fitting together some ideas that I have been unable to fit together. Ideas of Haidt, Glockner, Lerner, and Holyoak are notably connected. He conducted five experiments examining guilt and cooperation to test, in the most simple terms, the hypothesis that making moral judgments is closer to making an aesthetic judgment than to reasoning about the moral justifications of an action, and that moral intuitions come from moral emotions. The hypothesis is based on Jonathan Haidt’s idea that the role of reasoning is literally to provide reasons (or arguments) for the intuitively made judgment if there is a need to communicate it. Part of the hypothesis is also that emotional intuitions in moral decision-making are the result of compensatory information processing which follows principles of parallel constraint satisfaction (PCS). I am going to largely skip over the results of the experiments, but note that Stemmler believes that they support his hypothesis. He notes that guilt is only one emotion, but points out similarly confirming results for disgust.
This post is based on the paper: “The Affect Gap in Risky Choice: Affect-Rich Outcomes Attenuate Attention to Probability Information,” authored by Thorsten Pachur, Ralph Hertwig, and Roland Wolkewitz that appeared in Decision, 2013, Volume 1, No. 1, p 64-78. This is a continuation of the affect/ emotion theme. It is more of a valence based idea than Lerner’s Appraisal Tendency Framework. This is more thinking about emotion than actually experiencing it although the two can come together.
Often risky decisions involve outcomes that can create considerable emotional reactions. Should we travel by plane and tolerate a minimal risk of a fatal terrorist attack or take the car and run the risk of traffic jams and car accidents? How do people make such decisions? Decisions under risk typically obey the principle of the maximization of expectation.
The expectation expresses the average of an option’s outcomes, each weighted by its
probability. This, of course, underlies expected utility theory and cumulative prospect theory and these models do a good job in accounting for choices among relatively affect-poor
This post is based on a paper by Rebecca Ferrer, William Klein, Jennifer Lerner, Valerie Reyna, and Dacher Keltner: “Emotions and Health Decison-Making, Extending the Appraisal Tendency Framework to Improve Health and Healthcare,” in Behavioral Economics and Public Health, 2014. I note that Valerie Reyna is one of the authors of fuzzy trace theory (see post Fuzzy Trace Theory-Meaning, Memory, Development and subsequent posts.) which I find interesting.
The authors use the appraisal tendency framework (ATF) to predict how emotions may interact with situational factors to improve or degrade health-related decisions. The paper examines four categories of judgments and thought processes as related to health decisions: risk perception, valuation and reward-seeking, interpersonal attribution, and depth of information processing. They illustrate ways in which a better understanding of emotion can improve judgments and choices regarding health.
The ATF assumes that specific emotions give rise to corresponding cognitive and motivational processes that are related to the target of the emotion (i.e., the situation, person, or other stimulus that elicited the emotion). In contrast to theories that predict how broad mood states (positive or negative) may influence judgment and decision making, the ATF offers specific predictions for how discrete emotions will influence judgment and decision making (See Tables 1 and 2).
This is the second post based on a paper: “Emotion and Decision Making,” that is to appear in the 2014 Annual Review of Psychology. It was written by Jennifer S. Lerner, Ye Li, Piercarlo Valdesolo, and Karim Kassam.
David Hume: “Reason is, and ought only to be, the slave of the passions, and can never pretend to any other office than to serve and obey them.”
Still, most of us have made some bad decisions under the influence of emotion. There are unwanted effects of emotion on decision making, but as Lerner et al note, they can only sometimes be reduced.
The strategies to reduce unwanted effects broadly take one of two forms: (a) minimizing the magnitude of the emotional response (e.g., through time delay, reappraisal, or inducing a counteracting emotional state), or (b) insulating the judgment or decision process from the emotion (e.g., by crowding out emotion, increasing awareness of misattribution, or modifying the choice architecture).