Category Archives: Theory and Models

Nervous States –Promises

 

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.”

 

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Nervous States: Counterpoint

This is the second of three posts discussing William Davies’ book Nervous States–Democracy and the Decline of Reason. I pick a couple of areas to argue with some of the scenarios Davies presents.

Markets and Evolution

Davies discusses Hayek as the guy who believes in free markets above all else, and who has helped us reach this point of not agreeing on reality.  When I read Hayek (The Road to Serfdom), he said to me that free markets with the right stable rules in place are the best system for everyone. Unfortunately, determining the right stable rules is difficult and the job of government.  Hayek seems to have taken Adam Smith’s invisible hand and run with it. David Sloan Wilson in This View of Life- Completing the Darwinian Revolution makes clear that the invisible hand only works at one scale of a market (see posts Evolution for Everyone and Multilevel Selection Theory).

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Nervous States- Democracy and the Decline of Reason

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.

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Confidence, Part III

In Confidence, Part II, the authors conclude that confidence is computed continuously, online, throughout the decision making process, thus lending support to models of the mind as a device that computes with probabilistic estimates and probability distributions.

 

The Embodied Mind

One such explanation is that of predictive processing/embodied mind. Andy Clark, Jacob Hohwy, and Karl Friston have all helped to weave together this concept. Our minds are blends of top down and bottom up processing where error messages and the effort to fix those errors makes it possible for us to engage the world. According to the embodied mind model, our minds do not just reside in our heads. Our bodies determine how we interact with the world and how we shape our world so that we can predict better. Our evolutionary limitations have much to do with how our minds work. One example provided by Andy Clark and Barbara is a robot without any brain imitating human walking nearly perfectly (video go to 2:40). Now how does this tie into confidence?  Confidence at a conscious level is the extent of our belief that our decisions are correct. But the same thing is going on as a fundamental part of perception and action. Estimating the certainty of our own prediction error signals of our own mental states and processes is as Clark notes:  “clearly a delicate and tricky business. For it is the prediction error signal that…gets to ‘carry the news’.”

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Celiac Disease and the Prediction Machine

I discovered that I was a celiac a few months ago and accordingly I am on a gluten free diet.  Compared to most conditions discovered in one’s late sixties, celiac disease seems almost inconsequential. However, it fits into the idea of prediction error minimization.  In effect, the environment has changed and I need  to change my predictions. Bread and beer are now bad. My automatic, intuitive prediction machine has not been getting it right. It is disorienting. I can no longer “See food, eat food.” I can change the environment at home, but in the wider world I need to be aware. My brain needs to dedicate perpetual, and at least for now, conscious effort to this cause. It is almost as if I became instantly even dumber.  It makes me more self absorbed in social settings that involve food. Not known for my social skills, I have been a good listener, but now not so much.  On my Dad’s 94th birthday, I ate a big piece of German chocolate cake, enjoyed it thoroughly, and then remembered that it was not allowed. In my particular case, I do not get sick or nauseated when I make such a mistake so my commitment is always under threat. This demands an even larger share of my brain to be compliant. My main incentive to comply is those photos of my scalloped small intestine. I note that I was diagnosed after years of trying to figure out my low ferritin levels. (It will be extremely disappointing if I find that my ferritin is still low.) Continue reading

A Nice Surprise

This post is based on a paper written by Andy Clark, author of Surfing Uncertainty (See Paper Predictive Processing for a fuller treatment.),A nice surprise? Predictive processing and the active pursuit of novelty,”  that appeared in Phenomenology and the Cognitive Sciences, pp. 1-14. DOI: 10.1007/s11097-017-9525-z. For me this is a chance to learn how Andy Clark has polished up his arguments since his book.  It also strikes me as connected to my recent posts on Curiosity and Creativity.

Clark and Friston (See post The Prediction Machine) depict human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory simulations. But we know that we are attracted to the unexpected. We humans often seem to actively seek out surprising events, deliberately seeking novel and exciting streams of sensory stimulation. So how does that square with the idea of minimizing prediction error.

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Intuition and Creativity

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.

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Denver Bullet Study

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.

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Curiosity

 

Although I have had much respect for Dan Kahan’s work, I have had a little trouble with the Identity protective Cognition Thesis (ICT). The portion in bold in the quote below from “Motivated Numeracy and Enlightened Self-Government” has never rung true.

On matters like climate change, nuclear waste disposal, the financing of economic stimulus programs, and the like, an ordinary citizen pays no price for forming a perception of fact that is contrary to the best available empirical evidence: That individual’s personal beliefs and related actions—as consumer, voter, or public discussant—are too inconsequential to affect the level of risk he or anyone else faces or the outcome of any public policy debate. However, if he gets the ‘wrong answer” in relation to the one that is expected of members of his affinity group, the impact could be devastating: the loss of trust among peers, stigmatization within his community, and even the loss of economic opportunities.

Why should Thanksgiving be so painful if it were true? I do not even know what my friends think of these things. Now at some point issues like climate change become so politically tainted that you may avoid talking about them to not antagonize your friends, but that does not change my view. But now Kahan has a better explanation.

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Parallel Constraint Satisfaction Model updated with Learning

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.

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