In his latest book, The World Until Yesterday, Jared Diamond looks at how several societies that have avoided technological advancements over long periods of recent time can teach us something. He calls them traditional societies. He indicates that you can look at these traditional societies as separate experiments of how a society develops and maybe by picking and choosing, you can find ways to enhance today’s first world societies. Dealing with risk and making decisions are part of that.
This post summarizes the second half of a paper entitled: “Theory-informed design of values clarification methods: A cognitive psychological perspective on patient health-related decision making,” that appeared in Social Science & Medicine 77 (2013), and was written by Arwen H. Pieterse, Marieke de Vries, Marleen Kunneman, Anne M. Stiggelbout, and Deb Feldman-Stewart. The post includes a summary of the general agreements of the four theories and seven recommendations based on these agreements to aid value clarification for patients. Again, I think it is almost amazing that these theories are being examined in one paper. I am impressed by the clarity and usefulness of the examination.
This post provides the gist of the first half of a paper entitled: “Theory-informed design of values clarification methods: A cognitive psychological perspective on patient health-related decision making,” that appeared in Social Science & Medicine 77 (2013), and was written by Arwen H. Pieterse, Marieke de Vries, Marleen Kunneman, Anne M. Stiggelbout, and Deb Feldman-Stewart.
I, in a relatively clueless sense, have embraced the idea that automatic processes, intuitive, System 1, etc. make a lot of our decisions, especially that automatic processes are for output and deliberative processes are for input. (Intuition in J/DM) The paper titled: “Deliberation versus automaticity in decision making: Which presentation format features facilitate automatic decision making?” was written by Anke Söllner, Arndt Bröder, and Benjamin E. Hilbig and appeared in the May, 2013, issue of Judgment and Decision Making, tries to rain on my parade, at least a little.
This post looks at a paper by Andreas Glockner and Thorsten Pachur entitled: “Cognitive models of risky choice: Parameter stability and predictive accuracy of prospect theory,” that appeared in Cognition in 2012. The paper looks at the changeable parameters in prospect theory and tries to determine their explanatory value and also the extent which individuals have stable parameters. It also tests a number of heuristics along with expected value and expected utility theory by studying the responses of 66 college students at the University of Bonn.
Prospect theory is a descriptive model of decision making and considered by some the greatest psychological advance ever. A descriptive model tries to describe and predict actual behavior and not theoretically ideal behavior. It takes expected utility theory of Daniel Bernouilli who took the rational model of Blaise Pascal, and makes a couple of tweaks. It belongs to Daniel Kahneman and Amos Tversky, and earned Kahneman the Nobel Prize for economics. The tweaks are:
This post pulls out some of the more interesting findings from: “The Future of Memory: Remembering, Imagining, and the Brain,” in the November 2012 issue of Neuron. The authors, Daniel Schacter, Donna Addis, Demis Hassabis, Victoria Martin, R. Nathan Spreng, and Karl Szpunar examine recent research examining the role of memory in imagination and future thinking based on fMRI and behavioral studies. They have organized the literature with respect to four key points that have emerged from research reported since 2007: (1) it is important to distinguish between temporal and on temporal factors when conceptualizing processes involved in remembering the past and imagining the future; (2) despite impressive similarities between remembering the past and imagining the future, theoretically important differences have also emerged; (3) the component processes that comprise the default network supporting memory-based simulations are beginning to be identified; and (4) this network can couple flexibly with other networks to support complex goal-directed simulations.
Dan Gilbert, who was featured in the post on Regret, has done much work on affective forecasting. In many respects this post based on the paper co-written with T.D. Wilson, “Why the brain talks to itself: sources of error in emotional prediction,” and published in Phil. Trans. R. Soc. B (2009), is just a more generalized explanation of affective forecasting and its shortcomings. Gilbert is a good writer, but he likes a good analogy or one liner almost too much at times. In this case, Gilbert uses an analogy of Mark Twain working on his jokes and notes that the “When the human brain talks to itself, it does not always tell the truth.”