“Human achievement is lower when there are nonlinearities in the ecology.” (What has Brunswik’s Lens Model Taught?).
This post is inspired by the book: Rebooting AI – Building Artificial Intelligence We Can Trust, written by Gary Marcus and Ernest Davis, New York, 2019. Gary Marcus (see post Kluge) is a well known author and artificial intelligence entrepreneur and Ernest Davis is a professor of computer science at Carnegie Mellon. To oversimplify, the authors emphasize that the successes of AI are narrow and tend to be greedy, opaque, and brittle. They provide history of AI seemingly about being ready for prime time decade after decade after decade. Self driving cars are almost there, but they are not. Human frailties in driving result in a death about every 100,000,000 miles driven, but Marcus and Davis indicate that self driving cars require human intervention every 10,000 miles which is 10,000 times in 100,000,000 miles. It may be a very long time before we are ready to sign off on self-driving cars, because the progress thus far has been the easy part.
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 selections from: “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. I am going to become more familiar with the work of the authors since they clearly share my admiration for Hammond and were his colleagues. They also understand better than I how he fit into the discipline of judgment and decision making (The links take you to past Posts.). I merely cherry pick my opinion of his most significant contributions.
As a student of Egon Brunswik, Hammond advanced Brunswik’s theory of probabilistic functionalism and the idea of representative design. Hammond pioneered the use of Brunswik’s lens model as a framework for studying how individuals use information from the task environment to make judgments. Hammond introduced the lens model equation to the study of judgment processes, and used this to measure the utility of different forms of feedback in multiple-cue probability learning.
Hammond proposed cognitive continuum theory which states that quasirationality is an important middle-ground between intuition and analysis and that cognitive performance is dictated by the match between task properties and mode of cognition. Intuition (often also referred to as System 1, experiential, heuristic, and associative thinking) is generally considered to be an unconscious, implicit, automatic, holistic, fast process, with great capacity, requiring little cognitive effort. By contrast, analysis (often also referred to as System 2, rational, and rule-based thinking) is generally characterized as a conscious, explicit, controlled, deliberative, slow process that has limited capacity and is cognitively demanding. For Hammond, quasirationality is distinct from rationality. It comprises different combinations of intuition and analysis, and so may sometimes lie closer to the intuitive end of the cognitive continuum and at other times closer to the analytic end. Brunswik pointed to the adaptive nature of perception (and cognition). Dhami and Mumpower suggest that for Hammond, modes of cognition are determined by properties of the task (and/or expertise with the task). Task properties include, for example, the amount of information, its degree of redundancy, format, and order of presentation, as well as the decision maker’s familiarity with the task, opportunity for feedback, and extent of time pressure. The cognitive mode induced will depend on the number, nature and degree of task properties present.
Movement along the cognitive continuum is characterized as oscillatory or alternating, thus allowing different forms of compromise between intuition and analysis. Success on a task inhibits movement along the cognitive continuum (or change in cognitive mode) while failure stimulates it. In my opinion, Glöckner and his colleagues have built upon Hammond’s work. Parallel constraint satisfaction theory suggests that intuition and analysis operate in an integrative fashion and in concert with Hammond’s idea of oscillation between the two. Glockner suggests that intuition makes the decisions through an iterative lens model type process, but sends analysis out for more information when there is no clear winner.
Hammond returned to the themes of analysis and intuition and the cognitive continuum in his last book entitled Beyond Rationality: The Search for Wisdom in a Troubled Time, published at age 92 in 2007. This is a frank look at the world that pulls few punches. At the heart of his argument is the proposition that the key to wisdom lies in being able to match modes of cognition to properties of the task.
In 1996, Hammond published a book entitled Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice which attempted to understand the policy formation process. The book emphasized two key themes. The first theme was whether our decision making should be judged on coherence competence or on correspondence competence. The issue, according to Hammond, was whether in a policy context, it was more important to be rational (internally and logically consistent) or to be empirically accurate. Analysis is best judged with coherence, while intuition is best judged by accuracy. To achieve balance–quasirationality and eventually wisdom, the key lies in how we think about error, which was the second theme. Hammond emphasized the duality of error. Brunswik demonstrated that the error distributions for intuitive and analytical processes were quite different. Intuitive processes led to distributions in which there were few precisely correct responses but also few large errors, whereas with analysis there were often many precisely correct responses but occasional large errors. According to Hammond, duality of error inevitably occurs whenever decisions must be made in the face of irreducible uncertainty, or uncertainty that cannot be reduced at the moment action is required. Thus, there are two potential mistakes that may arise — false positives (Type I errors) and false negatives (Type II errors)—whenever policy decisions involve dichotomous choices, such as whether to admit or reject college applications, claims for welfare benefits, and so on. Hammond argued that any policy problem involving irreducible uncertainty has the potential for dual error, and consequently unavoidable injustice in which mistakes are made that favor one group over another. He identified two tools of particular value for analyzing policy making in the face of irreducible environmental uncertainty and duality of error. These were Signal Detection Theory and the Taylor-Russell paradigm. These concepts also applicable to best designing airplane instruments (See post Technology and the Ecological Hybrid.).
This post is based on a paper: “The Two Settings of Kind and Wicked Learning Environments” written by Robin M. Hogarth, Tomás Lejarraga, and Emre Soyer that appeared in Current Directions in Psychological Science 2015, Vol. 24(5) 379–385. Hogarth created the idea of kind and wicked learning environments and it is discussed in his book Educating Intuition.
Hogarth et al state that inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches.
David Brooks seems to be a fascination of mine. The New York Times columnist surprises me both in positive and negative ways. I only mention it when the surprise is negative. Below is an excerpt from his November 25, 2016, column.
And this is my problem with the cognitive sciences and the advice world generally. It’s built on the premise that we are chess masters who make decisions, for good or ill. But when it comes to the really major things we mostly follow our noses. What seems interesting, beautiful, curious and addicting?
Have you ever known anybody to turn away from anything they found compulsively engaging?
We don’t decide about life; we’re captured by life. In the major spheres, decision-making, when it happens at all, is downstream from curiosity and engagement. If we really want to understand and shape behavior, maybe we should look less at decision-making and more at curiosity. Why are you interested in the things you are interested in? Why are some people zealously seized, manically attentive and compulsively engaged?
Now that we know a bit more about decision-making, maybe the next frontier is desire. Maybe the next Kahneman and Tversky will help us understand what explains, fires and orders our loves.
I can imagine his frustration with the advice world and maybe with Kahneman and Tversky (see post Prospect Theory), but it appears that Brooks is only looking at the advice world. Brooks would benefit by looking at the work of Ken Hammond. The post Cognitive Continuum examines some of Hammond’s 1980 work. Hammond has those chess masters to whom Brooks refers as one extreme of the cognitive continuum. The post Intuition in J-DM looks at the work of Tilmann Betsch and Andreas Glockner in what is called Parallel Constraint Satisfaction theory.
Betsch and Glockner believe that information integration and output formation (choice, preference) is intuitive. Analysis involves directed search (looking for valid cues or asking an expert for advice), making sense of information, anticipating future events, etc. Thus, they see a judgment as a collaboration of intuition and analysis. The depth of analysis varies, but intuition is always working so preferences are formed even without intention. Limiting processing time and capacity constrains only input. Thus, once information is in the system, intuition will use that information irrespective of amount and capacity.
Curiosity might be considered the degree of dissonance we encounter in our automatic decision making that in effect tells us to analyze–find more information and examine it. We do mostly follow our noses, because it is adaptive. But it is also adaptive to be able to recognize change that is persistent and must be responded to. A parameter of the parallel constraint satisfaction model is the individual sensitivity to differences between cue validities. This implies that individuals respond differently to changing cue validities. Some change quickly when they perceive differences and others change at a glacial pace.
The post Rationality Defined Again: RUN & JUMP looks at the work of Tilmann Betsch and Carsten Held. Brooks in his opinion piece seems to be suggesting that analytic processing is pretty worthless. Betsch and Held have seen this before. They note that research on non-analytic processing has led some authors to conclude that intuition is superior to analysis or to at least promote it as such with the obvious example being Malcolm Gladwell in Blink. Such a notion, however, neglects the important role of decision context. The advantages and disadvantages of the different types of thought depend on the nature of the task. Moreover, the plea for a general superiority of intuition neglects the fact that analysis is capable of things that intuition is not. Consider, for example, the case of routine maintenance and deviation decisions. Routine decisions will lead to good results if prior experiences are representative for the task at hand. In a changing world, however, routines can become obsolete.
In the absence of analytic thought, adapting to changing contexts requires slow, repetitive learning. Upon encountering repeated failure, the individual’s behavioral tendencies will change. The virtue of deliberate analysis, Brooks’ chess mastering, lies in its power to quickly adapt to new situations without necessitating slow reinforcement learning. Whereas intuition is fast and holistic due to parallel processing, it is a slave to the pre-formed structure of knowledge as well as the representation of the decision problem. The relations among goals, situations, options and outcomes that result from prior knowledge provide the structural constraints under which intuitive processes operate. They can work very efficiently but, nevertheless, cannot change these constraint. The potential of analytic thought dwells in the power to change the structure of the representation of a decision problem.
I believe that Brooks realizes that analytic thought is one thing that distinguishes us from other creatures even though it does not seem to inform much of our decision making. The post Embodied(Grounded) prediction(cognition might also open a window for Brooks.
This post starts with the paper “Brains striving for coherence: Long-term cumulative plot formation in the default mode network,” authored by K. Tylén, P. Christensen, A. Roepstorff, T. Lund, S. Østergaard, and M. Donald. The paper appeared in NeuroImage 121 (2015) 106–114.
People are capable of navigating and keeping track of all the parallel social activities of everyday life even when confronted with interruptions or changes in the environment. Tylen et al suggest that even though these situations present themselves in series of interrupted segments often scattered over huge time periods, they tend to constitute perfectly well-formed and coherent experiences in conscious memory. However, the underlying mechanisms of such long-term integration is not well understood. While brain activity is generally traceable within the short time frame of working memory, these integrative processes last for minutes, hours or even days.
- Egon Brunswik’s Lens model as elucidated by Ken Hammond and examined by Karelaia & Hogarth (see post What has Brunswik’s Lens Model Taught? et al)
- Parallel Constraint Satisfaction model through Andreas Glockner and his colleagues (see post Parallel Constraint Satisfaction Theory et al)
- Surprise Minimization or Free Energy Minimization (see post Prediction Machine et al) as presented by Andy Clark and including the ideas of Karl Friston and others
I continually look for commment on and expansion of these ideas, and I often do this in the most lazy of ways, I google them. Recently I seemed to find the last two mentioned on the same page of a philosophy book. That was not actually true, but it did remind me of similarities that I could point out. The idea of a compensatory process where one changes his belief a little to match the current set of “facts” tracks well with the idea that we can get predictions correct by moving our hand to catch the ball so that it does not have to be thrown perfectly. Both clearly try to match up the environment and ourselves. The Parallel Constraint Satisfaction model minimizes dissonance while the Free Energy model minimizes surprise. Both dissonance and surprise can create instability. The Free Energy model is more universal than the Parallel Constraint Satisfaction model, while for decision making PCS is more precise. The Free Energy model also gives us the idea that heuristic models could fit within process models. All this points out what is obvious to us all. We need the right model for the right job.
This post is a look at the book by Philip E Tetlock and Dan Gardner, Superforecasting– the Art and Science of Prediction. Phil Tetlock is also the author of Expert Political Judgment: How Good Is It? How Can We Know? In Superforecasting Tetlock blends discussion of the largely popular literature on decision making and his long duration scientific work on the ability of experts and others to predict future events.
In Expert Political Judgment: How Good Is It? How Can We Know? Tetlock found that the average expert did little better than guessing. He also found that some did better. In Superforecasting he discusses the study of those who did better and how they did it.
This post looks at signal detection theory (SDT) once again. Ken Hammond helped me see the power of signal detection as a descriptive theory (post Irreducible Uncertainty..) The last year of news with respect to fatal encounters between the police and the public has made me think of signal detection again as quite relevant. I should note that Ken Hammond died in May 2015 and I am looking for his last paper “Concepts from Aeronautical Engineering Can Lead to Advances in Social Psychology”. This post is based on a paper: “Signal Detection by Human Observers: A Cutoff Reinforcement Learning Model of Categorization Decisions Under Uncertainty,” written by Ido Erev that appeared in the Journal of the American Psychological Association, 1998, Vol. 105, No. 2, 280-298. This paper is important, but dated.
Many common activities involve binary categorization decisions under uncertainty. The police must try to distinguish between the individuals who can and want to harm the public and/or the police from others. A doctor has to decide whether or not he should do more tests to see if you may have cancer. According to Erev, the frequent performance of categorization decisions and the observation that they can have high survival value suggest that the cognitive processes that determine these decisions should be simple and adaptive. Thus, it could be hypothesized that one basic (simple and adaptive) model can be used to describe these processes within a wide set of situations.