This post is based on the book, Elastic–Flexible Thinking in a Time of Change by Leonard Mlodinow, Pantheon Books, New York, 2018. Mlodinow is a physicist and worked with Stephen Hawking. His previous book Subliminal evidently gave him considerable access to interesting people like Seth MacFarlane. He mentions that Stephen Hawking’s pace of communicating was at best six words a minute with public presentations being done ahead of time. Mlodinow notes that this slowing of the pace of a conversation is actually quite helpful in forcing you to consider the words as opposed to thinking of what you are going to say while the other person is talking so that you can have an instant response.
Every couple of years, I seem to go back and look at “decision making” books that have arrived in my local library. I clearly take a broad view of decision making. This time I came up with Farsighted, Elastic, and the Mind is Flat. The first two books were definitely written to be popular books with the third less so. They share quite a bit. They all rely quite a bit on illustrations or questionnaires that show the peculiarities and shortcomings of our minds. They all rely on literature to explain their cases on how our minds work. Farsighted uses George Eliot and Middlemarch. Elastic uses Jonathan Franzen and mentions his book Corrections. The Mind is Flat uses Leo Tolstoy and Anna Karenina.
This post is based on a draft dated July 10, 2015, “Learning in Dynamic Probabilistic Environments: A Parallel-constraint Satisfaction Network-model Approach,” written by Marc Jekel, Andreas Glöckner, & Arndt Bröder. The paper includes experiments that contrast Parallel Constraint Satisfaction with the Adaptive Toolbox Approach. I have chosen to look only at the update of the PCS model with learning. The authors develop an integrative model for decision making and learning by extending previous work on parallel constraint satisfaction networks with algorithms of backward error-propagation learning. The Parallel Constraint Satisfaction Theory for Decision Making and Learning (PCS-DM-L) conceptualizes decision making as process of coherence structuring in which learning is achieved by adjusting network weights from one decision to the next. PCS-DM-L predicts that individuals adapt to the environment by gradual changes in cue weighting.
This post is based on a paper: “What is adaptive about adaptive decision making? A parallel constraint satisfaction account,” that was written by Andreas Glöckner, Benjamin E. Hilbig, and Marc Jekel and appeared in Cognition 133 (2014) 641–666. The paper is quite similar to that discussed in the post Swiss Army Knife or Adaptive Tool Box. However, it reflects an updated model that they call the PCS-DM model (parallel constraint satisfaction-decision making). From what I can tell this model attempts to address past weaknesses by describing the network structure more fully and does this at least partially by setting up a one-free parameter implementation which can accommodate individual differences and differences between tasks.