Thinking or Responding?

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https://bombbomb.com/blog/video-for-sales-thinking-fast-and-slow-kahneman/. Decision Making Under Uncertainty. Hideki Nomoto and David Slater.
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Thinking or Responding? Decision Making Under Uncertainty Hideki Nomoto and David Slater

DECISION MAKING UNDER UNCERTAINTY – THINKING OR RESPONDING Hideki Nomoto and David Slater

CONTEXT There seems to have been historically two different approaches to trying to understand and predict / model what goes on in the mind of a decision maker, particularly in risky and uncertain situations. The biological approach looks at the mind as a network of neurons and synapses, in which activities can be stimulated and monitored experimentally. From this data, processing centres can be inferred for different activities. What happens in those centres is currently very difficult to discern at a physical level as the system is just too complex. Nonetheless there is much valuable experimental data on the inputs and outputs of the system and the routing and involvement of various organs and structures in the body. The mind is thus treated as a “Black Box” whose inner workings are currently impenetrable, so we have little idea of what happens inside, physically when we think?)

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Some recent work (Koehne, Lipman) (Four, 2017) has tried to treat this physical network of neurons and synapses as a form of organic computer; and treat responses as “learned” algorithms, actual neural networks. They suggest that because of its complexity, an initial approach could treat the central nervous system as an interacting set of functions (as in thee software modelling approach, SADT) with inputs and outputs; without worrying too much about the physical details of their biological implementation. Their aim is to understand just enough about what processes happen inside the black boxes to enable the observed inputs and outputs to be predictable. The second softer approach owes much to experimental psychologists (Tversky, Kahneman, Slovic, et al) who studied how people behaved in different situations and developed ideas of how thinking can be identified with different patterns (Fast and Slow?) of behaviour and judgemental reactions to different situations. They developed theoretical models to explain decisions people were observed to make and infer what influenced the result of their thinking. Here then there is again, no need to specify how physically this happened just what decisions the mind (whatever and wherever that is) might make in different circumstances. In a recent note we have suggested trying to bridge this gap between physical (biological) and abstract ( conceptual) viewpoints, drawing upon the insights delivered by these two approaches and using current complex system modelling approaches (SADT and FRAM) to develop a better application of their findings. 2 FRMBRNK12 0.1

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THE FUNCTIONS OF THE MIND From Biology – the evolution of sophistication So from the biological point of view, there are a number of “black boxes” (BB’s) involved in a hierarchical (and physically distinct) arrangement of layers, which reflects the evolutionary development of organisms’ central nervous systems from the primitive, through higher animals to finally (or should it be currently), the human’s conscious, thinking mind. These layers (probably?) contain functions / BB’s such as:A. “Thinking” or Human Brain Layer 2 Reasoning, Insights Rationalisation B. “Conscious” or Higher animal Brain Layer 1 Perception Learning & Memorizing Motivation C. “Awareness” or Animal Brain Layer Arouse & Alert, Reflexes Motor Control D. Basic or Primitive Brain Layer Autonomic, Control Homeostasis

Figure 1– The Processing Levels of the Human Brain

In a recent note, (Slater D. , 2017) points out that these functions are also associated with specific structures and locations within the physical Brain. (for example, A. Neocortex, B. Hippocampus, C. Thalamus, and D. Stem). 3 FRMBRNK12 0.1

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Figure 2 – Certain Centres and organs can be identified for different “Activities”

From Psychology the (slow) thinking A and (fast) unthinking B responses In the more abstract, psychological explanations of the behaviour of the outputs of this complex system of neurons and synapses, there are again identified a number of “levels” at which responses are formulated. For example Kahneman et al. (Kahneman, 2005) postulate at least two levels of “thinking – Fast and Slow, or System 1 and System 2. In the former, decisions tend to be made on previously learned experiences or models (Heuristics and Biases), whereas more deliberate analysis assessment and reasoning tends to take more time or conscious effort (or both). Figure 3 – After Kahneman This also emphasises the “management” hierarchy implicit in the layers. D operates unless controlled by C,

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which in turn can be overridden by B and finally if there’s time to think countermanded by a more considered response from A.

Figure 4 – Are these the same functional Structures?

From Complex System (Functional) Modelling, the cascade of control Nomoto (Nomoto, 2017) has modelled the decision making needed to negotiate a challenging environment, walking across the concourse of Tokyo Station in the rush hour. In the FRAM

Figure 4 – The FRAM Function to Walk in the Station – after Nomoto

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methodology, the function – to walk, needs a number of outputs from functions which observe the environment, spot space, calculate whether it accessible before “firing” to produce an output to initiate the walk process. These are provided by other functions such as illustrated in Figure 5

Figure 5 – The interdependent Functions needed to “Walk”

Nomoto suggests that these functions actually form a hierarchy

Figure 6 – These influencing functions can be grouped into a Functional Hierarchy

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And this reflects again the Hierarchy observed in the Brain Structure

Figure 7 – The Nomoto Levels map on to the evolutionary order of physical Functions in the Brain

In the FRAM analysis, the analyst looks at the interactions needed for a particular action, reaction or process, and recognises in real life these can be very variable, even randomly variable. The analyst can then trace through the interactions and interdependencies to test what effect this variation could have on the outcome. Take for example, the Time Aspect of the FRAM function. If the Timing constraint is very tight – the time to react is limited, then the hard wired or genetic predisposition (the flight instinct in prey

Figure 8 – This hierarchical structure also is consistent with the Kahneman concepts

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species)l can take automatic control and respond. - (level D). If there is more time available, then a higher level function (say C) can operate to send an overriding control signal to the Action function and a different output could result. Clearly this scenario can apply in turn to the higher levels (B and then A), but on the timing hierarchy A is slower than B. (Echoes of Kahneman?) This then is a natural cascade of control of response, depending on Time available. The Inputs and outputs of this information processing system When the system responds to a stimulus (perhaps more than one), the system is essentially reacting to a simple binary signal. The information content needed then is either TRUE or FALSE (1 or 0; of course the strength of the signal needs to be within the (Goldilocks) range allowed. In the absence of further signals from other aspects the function will react and give its automatic response. But if there are other aspects affecting the function operability, whether this causes the function to fire will depend on the status of these other inputs and the probability of these being present, or not. So If the analyst asks what happens if the information transmitted and acted upon by the function is variable, the functions will have a probability of firing, not a simple binary output. So decisions, conscious or otherwise will be made on the basis of the probability that an action is required, based on the probable “meaning” of the combination of the information passed by all the interacting functions. Animals learn to expect certain outcomes based on experience (learned to estimate probability). For example we “know” that when throwing a dice, we have a 1 in 6 probability of it showing a particular face uppermost. But as the Reverend Bayes has pointed out in real life we would take in a number of additional pieces of information to judge the context of the gambling decision, For example, whether it was a fair dice and the reputation of the person throwing it. This is now a conditional probability. Ward Edwards (Edwards) has postulated that we do in fact take “slow” decisions based on conditional probabilities. But if we’re thinking “Fast”, but cannot always instinctively factor in other “conditions” in real time. Slater has shown that a FRAM model of the Brain functions is effectively a Bayesian Belief Net and thus we can model the effects and predictions from analyses in the same way that we should take decisions in real life as conditional probabilities. At the lower levels of the system, these probabilities are fairly straightforward. Some of the biological researchers have proposed that these stochastic probabilities are hard wired into fast response circuits.

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But at the higher levels, our tendency to think in unconditional probabilities (ref) can cause confusion.

THE JURY’S DILEMMA - DNA A good way to illustrate this is to look at a classic example of confusion, even by learned counsel! Imagine that a defendant is on trial solely because his DNA was found at a crime scene. The prosecution states - quite correctly - that the probability that an innocent person's DNA would match is one in a million. The jury is therefore invited to conclude that the defendant is almost certainly guilty. Though this argument has convinced many juries, nevertheless this last piece of reasoning is quite wrong. Even in a country with as few as (say) 10 million people there will be about 10 people whose DNA matches in that country alone, so the defendant is probably innocent. But if we use the Reverend Bayes’ approach (using the Open Group software- ref), we can build a simple model of the difference between conventional gambling probability thinking and the actual conditional probability thinking that is needed in this situation..

Our main "goal" is DNA found, meaning that DNA is found at the crime scene, and this could be caused either by the guilt of the accused, or a one-in-a-million chance. We simply make the goal DNA-found depend on guilt OR chance. The a-priori probability that a person about whom we know nothing is guilty is set at 1 in ten million, and the a-priori probability that their DNA will match by chance is set at 1 in a million.

Note that by cunning labelling the red and green bands show the probabilities that the words in the boxes are false or true, respectively. The probability report diagram above is when nothing else is specified. It's all red. In other words in absence of any special extra knowledge, there probably isn't a DNA match, the defendant is probably not guilty, and chance doesn't cause a match. This is not the situation in the courtroom.

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The probability report diagram above is when we know the defendant is guilty. DNA is found, but it wasn't caused by chance. Again this not the actual problem faced by the court, but the prosecution might want the jury to believe it is.

The court actually faces the problem illustrated in the last probability report diagram. Here we have included the extra information that DNA was found So the DNA-found entity is totally green because we know that's true. But when we look backwards into the causality we see that there is about a 10% probability that the dependent is guilty (the green in the guilt box) and about a 90% probability this was caused by chance (the green in the chance box). This kind of reasoning - reverse inference - is very important for risk analysis. It's also extremely difficult to get right by intuition. Making even simple inferences from statistics is fraught with danger, and that's the last thing we need when carrying out a risk analysis. So because our FRAM instantiations are essentially Bayesian Belief Nets (Slater), we have a model we can deploy quantitatively to try and understand what happens in real situations

FIREFIGHTERS DECISION MAKING IN EMERGENCY SITUATIONS Klein (Klein) in his book on insights (a level 2 activity) points out that most people in stress situations tend to react and make decisions using the Kahneman system 1, (level B) functions. This is where we draw on experience to predict probabilities of successful responses. The psychological analyses have pointed out that this can lead to us relying on “Heuristics and Biases”, which can be a problem if unrecognised. Holden (ref) has recently employed personal recording cameras (Body Cams) on firefighter commanders to record actual situations and the responses employed. Initial analyses tend to confirm that the level 1 thinking is generally observed. Klein has found the same in historical incidents and observes that it often takes a “desperation” stimulus to bring System 2 or A level processes into play. This way of modelling brain processes as a hierarchical array of interacting and interdependent functions fits well both the findings and conclusions of the purely physical biological and the interpretative theories of the observational psychologists. On a general levels the sorts of behaviours predicted fit well with what is seen in real life decision making.

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CONCLUSIONS Although there is extensive work in the literature on how the functions of the brain operate, it essentially falls into two categories.  

Experimental observation of the physical behaviour of the complex network of neurons which make up the biological implementation of our decision making system, and Developing theoretical explanations of the factors that cause us to behave in different ways.

But in order to gain some practical insights into how information is processed to enable us to take decisions in uncertainty, this paper has suggested utilising a purely functional modelling approach. This helpfully allows us to escape getting bogged down in the intricate details of the highly complex central nervous system. But its main advantage is that it allows us to probe and predict quantitavely how specific situations could affect resulting actions. This initial scoping discussion has found insights that fit well both with the physical experimental evidence and the current psychological approaches We argue then that it is worth utilising this FRAM BBN approach to further probe real life situations to compare with observations and to try and predict / improve decision making.

REFERENCES Ale, B. a. (2017, January). Risks and egulation- Risks we cannot afford? Researchgate. Retrieved from Researchgate: https://www.researchgate.net/publication/313472953_Risk_and_Regulation__Risks_we_cannot_afford Edwards, W. (n.d.). Scientific Decision Making. EU. (2016). Smart Resilience Indicators for Smart Critical Infrastructures. Stuttgart: IVL Report No. E 002. Four, B. (Director). (2017). The Immortalist [Motion Picture]. Hill, R. (2015). Functional Resonance Model Visualiser - FMV. Retrieved from Functional Resonance Analysis: www.functionalresonance.com/ Hollnagel, E. (2010). Safety I and Safety II. Ashgate. Hollnagel, E. (2011). FRAM: The functional resonance analysis method. Modelling Complex Sociotechnical Systems. Ashgate. Hollnagel, E. (2016). Resilience Analysis Graphs. Kahneman. (2005). Thinking Fast and Slaow. Klein. (n.d.). Insights. 11 FRMBRNK12 0.1

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Kletz, T. (2001). HAZOP anmd HAZAN. Chemical Engineer. Nomoto, H. (2017). Walking in Tokyo Station. FRAMily 2017. Rome: FRAMsynt. Slater, D. (2016). OPOPP - An Operability, Opportunity Study Manual. Researchgate https://www.researchgate.net/publication/313602214_AN_OPOPP_MANUAL_An_OPER ABILITYOPPORTUNITY_STUDY_PROTOCOL_USING_FRAM. Slater, D. (2017). The Functions of the Mind. Researchgate. Slater, D. A. (2015). On the Origins of PCDS - Probability Consequence Disagrams. Safety Science, 229 - 239. Woods, D. (2013). The Stress Strain Model of resilience Operationalises the four cornerstaones of Resilience Engineering. Resilience Engineering Association.

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