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Title

Decision paradoxes and biases explained by multicameral neural processing

Paradoxes and cognitive biases are explained by competing neural calculators

Risk analysis and risk communication in an evolutionary context

What biology and evolution tell us about risk communication

Human irrationality is a misperception

Evolution has not favored only the probability calculus

<<Authors should include anyone who contributes. First authorship should go to the person who champions the paper to get it written and submitted and revised.>>

Contributors (alphabetical):

Scott Ferson,

Christian Luhmann,

Jason O'Rawe,

Jack Siegrist,

W. Troy Tucker³,

Friends to consult/acknowledge: Frances White (Duke), Peter Wiedemann (Karlesrue), Donna Gresh (IBM)

Abstract <<315 words; 2261 characters>>

As widely recognized at least since Keynes and Knight, humans treat epistemic uncertainty (i.e., lack of knowledge or epistemic uncertainty) and variability (stochasticity or aleatory uncertainty) very differently. Recent clinical and neuroimaging evidence suggests that the multicameral human brain handles decision making under uncertainty by employing multiple separate neural processors. One of these processors is devoted to risk calculations based on frequency information while another processor handles detection and processing of epistemic uncertainty. A third processor attends to fairness calculations and cheater detection, which turn out to often play a significant role in the cognition of risks and uncertainty. These processors are localized to different parts of the brain, mediated by different chemical systems, and are separately activated by sensory stimuli that format uncertainty information. When multiple processors are activated simultaneously, they can give conflicting resolutions, but the brain may prioritize considerations of one of these calculators over another. We explore the effect that these sometimes competing processors have on perception and cognition of uncertainty and suggest that several famous paradoxes in probability and decision making may arise because of the interplay between these mental processors. These phenomena include lossaversion, ambiguity aversion and the Ellsberg Paradox, hyperbolic discounting, probability distortion, the two-dimensionality of risk perception, and others. Although these phenomena are usually presumed to be biases or cognitive illusions which are manifestations of human irrationality about risk and decision making, we describe why these phenomena may actually be advantageous in humans and other species. We also place them in an evolutionary context where, rather than being failings or limitations of the human brain, they instead seem to promote evolutionary fitness. The psychological and neurological evidence suggests that the two kinds of uncertainties should not be rolled up into one mathematical concept in risk assessment, but require a two-dimensional view that respects biological realities of human decision makers and the evolutionary forces that shaped them. << characters>>

Epistemic uncertainty, which is the result of poor measurements and incomplete knowledge, is often distinguished from aleatory uncertainty, which is caused by stochasticity or variability in the world. As recognized at least since Keynes and Knight, humans treat these two kinds of uncertainty very differently. When given simple frequency information, humans appear to make risk calculations in a manner consistent with probability theory and Bayesian norms, but the presence of even small epistemic uncertainty disrupts these calculations and produces decisions that typically focus exclusively on the worst-case outcomes, ignoring the available probably information. There are, in fact, many similar cognitive “biases and heuristics” that have been described by decision scientists and psychometricians over the last several decades, which are widely considered manifestations of human irrationality about risks and decision making.

Recent clinical and neuroimaging evidence suggest that humans are endowed with at least two special-purpose uncertainty processors in the multicameral human brain. One of these processors is devoted to risk calculations while the other handles detection and processing of epistemic uncertainty. These processors are localized in different parts of the brain and are mediated by different chemical systems that are separately activated by the format of sensory input. When both processors are activated, they can give conflicting resolutions, but the brain appears to often give priority to considerations of incertitude over variability.

We explore the effect that these competing processors have on perception and cognition of uncertainty and suggest that several famous paradoxes in probability and decision making may arise because of the interplay between these mental processors. These phenomena include loss aversion, ambiguity aversion and the Ellsberg Paradox, hyperbolic discounting, the two-dimensionality of risk perception, and others. Although these phenomena are usually presumed to be biases or cognitive illusions, we describe the adaptive significance of these phenomena in humans and other species and place them in an evolutionary context where they do not appear to be failings of the human brain but rather adaptations. The psychological and neurological evidence suggests that epistemic and aleatory uncertainty should not be rolled up into one mathematical concept in risk assessment, but require distinct approaches that respect biological realities within the decision-maker.

Keywords

probability paradoxes; cognitive biases; risk aversion; loss aversion; ambiguity aversion; Allais paradox; Ellsberg paradox; hyperbolic discounting; Ultimatum Game; probability neglect; cheater detection; fMRI neural imaging

Abstract <<271 words; 1904 characters>>

Decision scientists and psychometricians have described many cognitive biases over the last several decades, which are widely considered to be manifestations of human irrationality about risks and decision making. These phenomena include neglect of probability, loss aversion, probability distortion, hyperbolic discounting, ambiguity aversion and the Ellsberg Paradox, among many others. We suggest that all these and perhaps other recognised biases arise from the interplay between distinct special-purpose processors within the multicameral human brain whose existence is implied by recent clinical and neuroimaging evidence. Although these phenomena are usually presumed to be misperceptions or cognitive illusions, we describe the evolutionary significance of these phenomena in humans and other species, and we place them in their biological context where they do not appear to be failings of the human brain but rather evolutionary adaptations to life in an uncertain and risky world. Apparent paradoxes arise when psychometricians attempt to interpret human behaviors against the inappropriate norm of the theory of probability, which turns out to be an overly precise calculus of uncertainty when different mental processors give contradictory results. This view of the psychological and neurological evidence also suggests why risk communication efforts so often dramatically fail and how they might be substantially improved. For instance, it now seems clear that what risk analysts call epistemic uncertainty (i.e., lack of knowledge or ambiguity) and aleatory uncertainty (variation or stochasticity) should not be rolled up into one mathematical probabilistic concept in risk assessments, but they instead require an analysis that distinguishes them and keeps them separate in a way that respects the cognitive functions within the decision makers to whom risk communications are directed.

Introduction

Alan Alda: "I've wondered about this for a long time...Is it possible that you have competing parts of the brain, each looking for dominance, and one may chose a path that is totally against the probabilities but if suppressed by other areas would make a more rational choice?"

Knight

Keynes

Incertitude = epistemic uncertainty

Arises from incomplete knowledge

Incertitude arises from

limited sample size

mensurational limits (‘measurement uncertainty’)

use of surrogate data

Reducible with empirical effort

Variability = aleatory uncertainty

Arises from natural stochasticity

Variability arises from

spatial variation

temporal fluctuations

manufacturing or genetic differences

Not reducible by empirical effort

Variability

Arises from natural stochasticity

Variability arises from

spatial variation

temporal fluctuations

manufacturing or genetic differences

Not reducible by empirical effort

Incertitude

Arises from incomplete knowledge

Incertitude arises from

limited sample size

mensurational limits (‘measurement error’)

use of surrogate data

Reducible with empirical effort

Two kinds of uncertainty

Freezing and procrastination is a common response of stimulation of the amygdala. There are several advantages of such behavior.

See https://sites.google.com/site/workinguncertain/say/procrastinationfosterscreativity

Propagating incertitude

A few grossly uncertain inputs

A lot of grossly uncertain inputs...

Traditional approach fails incertitude

Traditional probability theory doesn’t account for gross uncertainty correctly

Output precision depends strongly on the number inputs and not so much on their shapes

The more inputs, the tighter the answer and the more serious the error

Probability is hard

Probability is a very young discipline

Two centuries old, other branches of math >22

Only invented for resolving games of chance

Probability is famously counterintuitive

Monty Hall problem embarrassed prominent scholars

Even experts make egregious mistakes (e.g., Laplace)

De Morgan left probability because it was too hard

Rife with paradoxes, unlike any other branch of math

Probability paradoxes

Ellsberg paradox

St. Petersburg paradox

Two-envelopes problem

Monty Hall problem

Simpson’s paradox

Bertand paradox

Berkson’s paradox

Sleeping Beauty problem

Cognitive biases

Psychometry has documented cognitive biases in humans that make us prone to judgment errors because of the way our brains are wired

Groupthink  Stereotyping

Memory flaws  Optical illusions

Kahneman and Tversky reviewed many such biases in how humans perceive risks and uncertainties and make decisions

Decision biases

Ambiguity aversion (Ellsberg paradox)

Avoiding options when probabilities seem unknown

Loss aversion

Disliking a loss more fervently than liking a gain of the same magnitude

Zero-risk bias

Preferring to reduce a small risk to zero over a greater reduction in a larger risk

Anchoring

Relying too heavily on a past reference or one piece of information

Inaction aversion

Insisting on “doing something” even if the optimal decision to stand down

Risk aversion (Allais paradox)

Prefer a sure thing over a gamble with an equivalent or higher expectation

Availability heuristic

Estimating likelihood of something by the ease with which it’s remembered

Uncertainty biases

Probability misperception

Overestimating chance of rare outcomes, understating chances of common ones

Conjunction fallacy

Assuming that specific conditions are more probable than general ones

Pseudocertainty

Making risk-averse choices for positive outcomes, but risk-seeking for negative

Overconfidence

Excessive confidence in one’s own predictions

Base rate fallacy

Neglecting available statistical data in favor of particulars

Neglect of probability

Disregarding probability in decision making under uncertainty

Other biases

Clustering illusion

Seeing patterns in noise

Gambler's fallacy

Thinking future probabilities are altered by past events, e.g., P(head | 4 tails)

Framing

Drawing different conclusions based on how data are presented

Regression toward the mean

Expecting extreme performance to continue

Ludic fallacy

Believing that chance in life is like chance in games

Primacy

Weighting initial events more than subsequent events

Recency

Weighting recent events more than earlier events

Hyperbolic discounting

Strongly preferring immediate payoffs over later (more intense the closer to the present payoffs are

Heuristics

These biases are presumed to be the result of using mental shortcuts, called “heuristics”

Thus humans’ misconceptions are the results of bad wiring in our brains

And, apparently, people are especially stupid about risks and uncertainty

But how can this be?

If our biases are so bad, how is it that humans have been so successful evolutionarily?

Risks and uncertainty have likely been pretty prominent in evolution over the last 106 years

Luce noted the main finding of decision theory is that humans don’t make decisions like decision theory says they should

Neuroscience of risk perception

Instead of being divided into rational and emotional sides, the human brain has many special-purpose calculators

(Marr 1982; Barkow et al. 1992; Pinker 1997, 2002)

The format of sensory data triggers a calculator

(e.g. Cosmides & Tooby 1996; Gigerenzer 1991)

Different calculators give competing

solutions, or calculate different

components of total risk

(e.g. Glimcher & Rustichini 2004 and references therein)

Multiple calculators may fire

There are distinct calculators associated with

Probabilities and risk (variability) medical students

Ambiguity and uncertainty (incertitude) Hsu et al.

Trust and fairness (cheater detection) Ultimatum Game

Brain processes them differently

Different parts of the brain

Different chemical systems

They can give conflicting responses

Risk aversion

You’ll get $1000 if a random ball is red, or, you can just get $500 now

Which prize do you want?

Ambiguity aversion

Balls can be either red or blue

Two urns, both with 36 balls

Get $1000 if a random ball is red

Which urn do you wanna draw from?

Ellsberg Paradox

Balls can be red, blue or yellow (probs are R, B, Y )

The urn has 30 red balls and 60 other balls

Don’t know how many are blue, how many are yellow

Gamble A Gamble B

Get $1000 if draw red Get $1000 if draw blue

Gamble C Gamble D

Get $1000 if red or yellow Get $1000 if blue or yellow

Persistent paradox

People always prefer unambiguous outcomes

Doesn’t depend on your utility function or payoff

Not related to risk aversion

Not explained by probability theory, or by prospect theory

Most people prefer A to B (so are saying R.> B) but also prefer D to C (saying R < B)

Other species

Chimpanzees and bonobos preferred peanuts (which they like less than bananas) when they don’t know the probability of getting bananas

fMRI

Hsu et al. (2005) found localized regions of activity in the brain under situations of ambiguity (incertitude)

Amygdala associated with processing fear and threat

Ambiguity/incertitude detector

Humans have an incertitude processor

Triggered by situations with ambiguity

Especially focused on the worst case

Common response is procrastination

Best comments:

I'll watch this later... Posted by: theonlyhankinla | Sep 19, 2012 11:32:33 AM

I was going to write that, but I waited too long. Posted by: Critifur | Sep 19, 2012 11:58:27 AM

This video presumes procrastination is essentially a disease or a bad habit. But, in fact, procrastination can be a advantageous behavior, especially in the context of uncertainty in which danger can lurk.

The early bird gets the worm, but the second mouse gets the cheese.

The Guardian notes

Procrastination has several substantial advantages including

better outcomes

better to use all and latest info, which may not be available initially

automatically prioritizes

put off boring work until it can be displacement behavior for something even worse

avoids unnecessary work

sometimes the work doesn't need to be done after all, so doing it early is a waste; Kedrosky described a “nagging suspicion that a lot of the things that I get asked to do I don’t actually have to do.”

it's more fun and more efficient

See also essays at Fluent Time Management and Alternet.

Functional organ

Normal feature of the human brain

Not a product of learning

Visible in fMRI

Brain lesions can make people insensitive to incertitude…so they behave as Bayesians

But why pessimism?

Pessimism is often advantageous evolutionarily

Natural selection can favor pessimism

Death is ‘hard selection’

Animal foraging strategies

Programmed plant behaviors

Being wrong often has asymmetric consequences

Foraging: Finding dinner versus being dinner

Competition: Preemption versus being preempted

Collisions of the two cameras

Ambiguity aversion (Ellsberg paradox)

Probability neglect

Loss aversion

Framing effects

Hyperbolic discounting

Two-envelopes problem

Slovic’s two-dimensional plot of risks

The third fairness calculator explains even more

Human irrationality

Risk analysis from a biological perspective

Communication Krebs)

Behavior by a speaker to evoke a reaction in a hearer

Not to “share information”

Not altruistic

Reasoning (BBS paper this year)

Mechanism by which we craft arguments that will be compelling to a listener

Not to “uncover truth”

Not free from fallacy

Risk analysts are self-deluded

Risk analysts may believe themselves to be dispassionate and disinterested

But people implicitly know risk communicators are trying to get them to do something

They also know that analysts’ numbers could be wrong…or they could be lying

Risk analysts need to publically acknowledge so

Take-home messages

Humans are wired by evolution to process incertitude separately and differently from variability

This explains many “paradoxes” and “biases”

Risk analysis must (and can) distinguish them

Risk analysis should be more attuned to the biological realities of human cognition

End

Probability bounds analysis (PBA)

Convenient version of imprecise probability

Bridge between qualitative and quantitative

Sidesteps the major criticisms

Doesn’t force you to make any assumptions

Uses only whatever information is available

Distinguishes variability and incertitude

Merges interval and probabilistic analysis

Used by both Bayesians and frequentists

Probability box (p-box)

P-boxes

We can do maths with p-boxes

A = {lognormal, mean = [.05,.06], variance = [.0001,.001])

B = {min = 0, max = 0.05, mode = 0.03}

C = {sample data = 0.2, 0.5, 0.6, 0.7, 0.75, 0.8}

D = [0, 1]

Probability of A & B & C & D

Is there any evolutionary advantage to behaving in this way? Is it, in some sense, the right reaction to the problem, despite the normativity claims of the proponents of the subjective expected utility theory? Is there an evolutionary selective advantage to processing incertitude (or ambiguity, or epistemic uncertainty or just ignorance) differently than the way we process risks that arises from variability? Given that the amygdala <<>> is not vestigial in humans, this seems possible.

<<demonstration: snakes>>

<<Montauk guidance>>

<<language from the ARRA proposal>>

<<Parthenon>>

<<

1) ambiguity aversion; Ellsberg paradox

2) medical students and Bayes' rule

3) ultimatum game

4) loss aversion

5) systematicity of probability over-, understatement

6) long list of paradoxes and biases

>>

<<email Pinker>>

<<"play it safe" means to refuse to gamble when the odds are uncertain>>

<<The Hunger Games>> cooperation; even in hypercompetition, cooperation is essential

<<Looper>> hyperbolicity; see also Keith Chen's theory about language's influence on perception and thus behavior (Pinker must disapprove of this Worfian idea):

September 26, 2014

Pinker, S. (2014). Why academics stink at writing. The Chronical of Higher Education, The Chronical Review. http://chronicle.com/article/Why-Academics-Writing-Stinks/148989/

"The guiding metaphor of classic style is seeing the world. The writer can see something that the reader has not yet noticed, and he orients the reader so she can see for herself. The purpose of writing is presentation, and its motive is disinterested truth. It succeeds when it aligns language with truth, the proof of success being clarity and simplicity. The truth can be known and is not the same as the language that reveals it; prose is a window onto the world. The writer knows the truth before putting it into words; he is not using the occasion of writing to sort out what he thinks. The writer and the reader are equals: The reader can recognize the truth when she sees it, as long as she is given an unobstructed view. And the process of directing the reader’s gaze takes the form of a conversation."

This is not the real purpose of scientific writing or speaking, and lay people know it even if scientists do not.

>>

Acknowledgments

This paper was written as a part of a Small Business Innovation Research grant (RC3LM010794) from the National Library of Medicine, a component of the National Institutes of Health, funded under the American Recovery and Reinvestment Act. The opinions expressed herein are those of the authors and do not reflect the positions of the funding agency.

References

Ermer, E., S.A. Guerin, L. Cosmides, J. Tooby, and M.B. Miller (2006). Theory of mind broad and narrow: reasoning about social exchange engages ToM areas, precautionary reasoning does not. Social Neuroscience 1: 196–.219. http://www.psych.ucsb.edu/research/cep/papers/TOMbroadnarrow.pdf

Woodpile

This material is not used in the paper.

language of infection: food next to rat poison in the grocery bag

Scott Ferson

Jul 31, 2012•Comments off

http://www.ncbi.nlm.nih.gov/m/pubmed/21673807/

Of black swans and tossed coins: is the description-experience gap in risky choice limited to rare events?

Authors

Ludvig EA, et al. Show all

Journal

PLoS One. 2011;6(6):e20262. Epub 2011 Jun 1.

Affiliation

Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America. eludvig@princeton.edu

Abstract

When faced with risky decisions, people tend to be risk averse for gains and risk seeking for losses (the reflection effect). Studies examining this risk-sensitive decision making, however, typically ask people directly what they would do in hypothetical choice scenarios. A recent flurry of studies has shown that when these risky decisions include rare outcomes, people make different choices for explicitly described probabilities than for experienced probabilistic outcomes. Specifically, rare outcomes are overweighted when described and underweighted when experienced. In two experiments, we examined risk-sensitive decision making when the risky option had two equally probable (50%) outcomes. For experience-based decisions, there was a reversal of the reflection effect with greater risk seeking for gains than for losses, as compared to description-based decisions. This fundamental difference in experienced and described choices cannot be explained by the weighting of rare events and suggests a separate subjective utility curve for experience.

PMID

21673807 [PubMed - indexed for MEDLINE]

PMCID

PMC3105996 Free Full Text

Free full text: Public Library of Science

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Biological and evolutionary context of risk analysis

About

This site collects material for a review paper(s) defining risk analysis and communication from a biological and evolutionary perspective, and explaining human cognitive biases and heuristics as features of our multicameral brain.

Acknowledgments

Support for this project was provided by the National Library of Medicine, a component of the National Institutes of Health (NIH), through a Small Business Innovation Research grant (award number RC3LM010794) to Applied Biomathematics funded under the American Recovery and Reinvestment Act.

Disclaimer

The views and opinions expressed herein are tentative and pre-decisional, and should not be considered those of any of the authors or collaborators, nor of Applied Biomathematics, the National Library of Medicine, National Institutes of Health or other sponsors.

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