Introduction to human heuristics by Franco Bagnoli

www.aware-project.eu

Introduction to Human HeuristicsMaterial for social and pervasive computing

Franco Bagnoli & Andrea Guazzini

Center for the Study of Complex DynamicsUniversity of Firenze, Italywww.complexworld.net

Introduction

Humans do not deal with problems in a “rational” way. They use“rules of thumb” called heuristics, which are more “economic” thanfull rationality, but sometimes fail spectacularly.

Our brain has been selected in a social environment, and we havedeveloped heuristics to solve social problems, in limited time, withlimited computational capabilities and with limited informationavailable.

Autonomous agents and portable devices are often confronted withsimilar situations, so the adaptation of human decision systems tocomputer science might be fruitful.

Moreover, autonomous devices have often to collaborate withhumans, and even act in their delegation.

Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 2 / 22

Are humans smart?

Humans love to think to be intelligent and to take rational decisions.

Actually, rational thinking is quite slow and computationaldemanding. We can discriminate the “usage” of cognitive capabilitiesby fMRI and response times. For instance, a good ping-pong playernever “thinks” to the next move.

Some partially “blind” people (blind sight) can detect movementseven if they cannot “understand” what they see.

Human recognition need “emotional” components, otherwise thesubjects cannot even recognise themselves in a mirror.

The signals that initiate a voluntary movement starts about 0.35 searlier than the subject’s reported conscious awareness that he/she isfeeling the desire to make a movement. Do we have free will in theinitiation of our movements? Since subjects were able to preventintended movement at the last moment, we surely do have a vetopossibility.


Heuristics as weak intelligence

We have to take a lot of decisions in everyday life.

Generally, these decision are satisfactory, but we all experiencefrustration for having chosen the bad choice, or having been cheated.

Twerski and Kahneman examined many situations, and pointed outthe existence of heuristics: “rules of thumb” that are used everyday,like for instance “prejudicial judgements” based on appearances.

Clearly, if applied to a wrong context, heuristics may fail spectacularly.

Heuristics may be hard-coded (and therefore sometimes calledschemes) or learned.


Examples of classic heuristics: anchoring

When taking a decision, we rarely “weight” all factors, and generallyrely heavily on just one piece of information (the one easier to recall),and only in a second moment we “adjust” the answer according toother factors.

A classical example is the question “Estimate the probability of deathby lung cancer and by vehicle accidents”. People tends to assign ahigher probability to car accidents (since they are much morecommonly reported by press) but lung cancer causes about 3 timesmore deaths than cars.

If one asks if Turkey population is more or less than 30 million, andthen asks to estimate that population, the average will be around thatfigure (Turkey has about 75 million population).


Representativeness

People are insensitive to prior probability of outcomes They ignorepreexisting distribution of categories or base rate frequencies. Bayes’theorem is not easily understood.

People are insensitive to sample size They draw strong inferencesfrom small number of cases

People have a misconception of chance: gambler’s fallacy. They thinkchance will “correct” a series of “rare” events.

People have a misconception of regression. they deny chance as afactor causing extreme outcome.


Representativeness examples

Is the roulette sequence “6, 6, 6” more or less probable than “10, 27,36”?

All kind of stereotypes: black people vs. white people, immigrants,etc.

There is a murder in New York, and the DNA test (say 99.99%accuracy both for false positive and false negatives) is positive for thedefendant. There are no other cues. Which is the probability that thedefendant is guilty?


Heuristics as fast and frugal processing

At present, heuristics have a better reputation: they can beconsidered as “optimized” methods of saving computationalresourced and giving faster answers (Gigerenzer).

Many everyday problems would require “unbounded” rationality to besolved, and a large time for samplig all possibilities.

But we do not try every possible partner when choosing a mate (nor atiny fraction of them...).

In a variable world, sometimes the “rules of thumb” are really betterthen the weighted methods taught by economists.

In real world, with redundant information, Bayes’ theorem and“rational” algorithms quickly become mathematically complex andcomputationally intractable.


A new view of heuristics

Ecologically rational (that is, they exploit structures of information inthe environment).

Founded in evolved psychological capacities such as memory and theperceptual system.

Simple enough to operate effectively when time, knowledge, andcomputational might are limited.

Precise enough to be modelled computationally

Powerful enough to model both good and poor reasoning.

(Goldstein & Gigerenzer, 2004)


Recognition heuristics

In 1991 Gigerenzer and Goldstein asked twelve students in Californiaand Germany to estimate whether S. Diego or S. Antonio had a largerpopulation. German students were much more accurate, simplybecause most of them did not know S. Antonio.

The same test was performed on soccer outcome, financial estimates,etc.

But Oppenheim (2003) showed that we use also other cues. If askedto judge between a known little city and a fictitious one, most ofpeople would choose the non-existing city.

In any case, there is information in ignorance (and probablyadvantages in forgetting).


Take the best

We often have to choose the “best” (buy a new car).

The most rational thing to do is to maximise a weighted score. Theweights can be extracted by past experiences.

For instance: you are a physicians and have to decide whether a manwith severe chest pain should be sent to the coronary care unit or aregular nursing bed.

The method based on weighted decision was slow, and had anefficiency of nearly 50% (i.e., random choice).

A simpler decision tree is much more effective: first consider the mostimportant factor – had the patient already experienced hart attacks?If yes, go to intensive unit. Then the second: is the pain localized inchest? If yes, go to intensive unit, etc. etc.

This is why advertisers focus on “irrelevant” details for selling cars...


Where do heuristics come from?

Heuristics, like all our brain, is a product of selection.

We are at hand with natural selection, i.e., competition for surviving.But in order to select a trait in this way, nature has to literally killeveryone not carrying that trait before reproductive age.

A much less cruel but more effective selection is the sexual one.

In many species, just a tiny fraction of individuals (the leading male,for instance) do actually reproduce.


Sexual selection

Sexual selection is so effective, that a tiny improvement in attractingthe opposite sex can result in larger offspring.

This is the origin of the extreme sexual ornaments found in allsexually-reproducing species.

For humans, the principal ornaments are (probably) power anddexterity (mainly linguistic): poetry, songs,...

It has been suggested that our “large” brain (with art and all uselessbrain products) is just a sexual ornament.


Machiavellic brain

Monkey and ape societies are often complex social systems.

In such cases, the leading position is conquered by means of alliances,not by pure muscle power.

This implies large cognitive power, since one needs to elaborate notonly information about others, but also their mutual relationships.

Actually, the size of frontal cortex (the “monkey” brain) correlateswell with the group size (from which one obtains the Dunbar numberfor the human group size).


Logic brain

We find logic problems hard.

How many cards should one turn (at minimum) to check if the followingrule is violated?

Cards with odd digits have a vocal on the back.


Social brain

But social tasks are easier...

How many cards should one turn (at minimum) to check if the followingrule is violated?

People less than 18 cannot drink alcohol.


Cooperative brain

We have developed sophisticated methods for eliciting cooperationand punishing defeaters.

Not surprisingly, this opens the way to (repeated) game theory...


Example: the ultimatum game

In this game, you are given 10$, and you have to decide how manydollars you will offer to a third person. He/she can accept and youshare the money, or he/she can refuse and in this case both of youwill loose everything.

How much would you offer?

If you were the third person, up to how much would you accept?

What is the most rational thing to do?


The dictator game

This is the same as the ultimatum, but in this case the third personcannot refuse.

How much would you offer in this case?

Before answering, consider the following possibilities:

This third person is sitting near to you.This third person is somewhere far from you.You personally know this person and you know that in some futuretime he/she can play you present role.You know that you’ll never meet again this person.You know that your choice will be made public in your school/office.



The trust game

This is the same as the dictator, but in this case the third personinitially offers some amount of money, which is doubled by the gamemanager. The dictator can decide to give back (partially) or keep forhim/her-self.

How much would you offer initially in this case (third person initialmove)?

Suppose you are offered 5$, which become 10. How much would offerback if you were the dictator?



This is the end...

There is a network of nodes that process information coming fromneighbors.

The information can be corrupted, and in this case also theelaborated information is tainted, like a disease. The node remainsinfected only for a limited time.

A node can check the correctness of the received information on acentral repository, but it is costly (say, it takes time).

Try to develop an heuristic for deciding when information should bechecked.

What additional information might be useful for reducing theinfection level while not wasting resources in consultations? The“trustability” of neighbours? The average level of infection? Howlong should the memory last?

How do these solutions depend on the geometry of the network?What does happen on a regular lattice/disordered graph/scale freenetworks?


E-mail your proposal to [email protected]

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Introduction to human heuristics by Franco Bagnoli