searching for the second foundation

Discovering the Second Foundation

The origins of sociophysics

Franco Bagnoli1 Andrea Guazzini1,2

(1) Laboratorio Fisica dei Sistemi Complessi - Dip. Energeticaand CSDC, Universita di Firenze

(2) IIT, CNR, Pisa

Bagnoli, Guazzini (CSDC & IIT) Second Foundation 17 novembre 2011 1 / 32

The Foundations Cycle

In 1942 Asimov started the Foundations cycle.

Inspired by The History of the Decline andFall of the Roman Empire (EdwardGibbon), the cycle spans about 500 years ofhistory in a far future.

The story begins when Hari Seldon, amathematician, proves the theoreticalpossibility to predict the future of a societyon a mathematical basis.

After various vicissitudes, Hari manages toestablish two foundations, at the “oppositeends of the galaxy”. The goal is that ofshortening the period of chaos after theexpected fall of the Galactic Empire from theestimated 30,000 years to only 1,000 years.


The Foundations

The two foundations have very different tasks. The first is asked topreserve the technical knowledge, and is destined to dominate thenearby planets (and then the whole ex-empire).

The Second Foundation instead must remain secret. It is formed bymathematicians, who have the task of writing the equations whichmodel in detail the future of the humanity, and correcting the“deviations”.Of course not all goes smoothly. The FirstFoundation must face some Seldon crisis(bifurcation points?), and they succeed with thesecret help of the Second Foundation, which hasdeveloped psychological methods to manipulatepeople.

There is also a war of the First Foundation againstthe Second, because the former do not want to bemanipulated by the latter.


FuturICT

Only fantasies? The flagship project FuturICT, which could be financedby the European Union with 1 billion euros in 10 years, seeks similar goals.

FuturICT will build a sophisticated simulation, visualization andparticipation platform, called the living earth platform( planetary-scale data collection and simulations). Thisplatform will power crisis observatories, to detect and mitigatecrises, and participatory platforms, to support thedecision-making of policy-makers, business people and citizens,and to facilitate a better social, economic and politicalparticipation.

Not very different from Asimov’s novels.Bagnoli, Guazzini (CSDC & IIT) Second Foundation 17 novembre 2011 4 / 32

The psycohistory

The fundamental intuition of Hari Seldon is that the various planetscan be considered uncorrelated.Asimov was a chemist, and in fact thisapproximation is the basis of “chemical” equations(mass law). He says explicity: psycohistory is likethe gas law for humans.

But we know that microscopic symmetries andconstraints reflect on macroscopic behavior.

And as happens in chemistry, Asimov-Seldonrecommends to apply these equations only to verylarge populations (and to keep this practicesecret!).

So, the main question is: which is the simplestmodel of an human?


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 cognitivecapabilities by fMRI and response times. For instance, a goodping-pong player never “thinks” to the next move.

We cannot avoid unconsciousknowledge. Some partially “blind”people (blind sight) can detectmovements even if they cannot“understand” what they see.

Human recognition needs emotionalcomponents, otherwise the subjectscannot even recognise themselves ina mirror.


Heuristics as weak intelligence

We have to take a lot of decisions ineveryday life.

Generally, these decision aresatisfactory, but we all experiencefrustration for having chosen the badchoice, or having been cheated.

Twerski and Kahneman pointed outthe existence of heuristics: rules ofthumb that are used everyday, like forinstance “prejudicial judgements” basedon appearances.

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


Examples of classic heuristics: anchoring

When taking a decision, we heavily rely on just one piece ofinformation (the one easier to recall), and only in a second momentwe “adjust” the answer according to other factors.

A classical example is the question Estimate the probability ofdeath by lung cancer and by vehicle accidents.

People tends to assign a higherprobability to car accidents (sincethey are much more commonlyreported by press) but lungcancer causes about 3 timesmore deaths than cars.


Representativeness

Although we always assume a probabilistic world, we are insensitive toprior probability of outcomes. We ignore preexisting distribution ofcategories or base rate frequencies. Bayes’ theorem is not easilyunderstood.

We are insensitive to sample size. We tend to draw strong inferencesfrom small number of casesWe have a misconception of chance:gambler’s fallacy. We think thatchance will “correct” a series of “rare”events.

We have a misconception of regression.We deny chance as a factor causingextreme outcome.


Representativeness examples

Is the roulette sequence “6, 6, 6” mo-re or less probable than “10, 27, 36”?After six “6”’s, would you prefer tobet on the 6 or on any other number?

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

There is a murder in New York, andthe DNA test (say 99.99% accuracyboth for false positive and false ne-gatives) is positive for the defendant.There are no other cues. Which isthe probability that the defendant isguilty?


Heuristics as fast and frugal processing

At present, heuristics have a better : they can be considered asoptimized methods of saving computational resourced and givingfaster 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 (nora tiny fraction of them...).

In a variable world, sometimes the “rules ofthumb” are really better then the weightedmethods taught by economists.

In real world, with redundant information,Bayes’ theorem and “rational” algorithmsquickly become mathematically complexand computationally intractable.


Recognition heuristics

In 1991 Gigerenzer and Goldstein asked students in California andGermany 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, financialestimates, etc.

Which town has a largerpopulation?

A BErfurt WittenTrier Duisburg

Bochum NeussKrefeld Leipzig

Darmstadt MannheimCottbus Rostock

But Oppenheim (2003) showedthat we use also other cues. Ifasked to judge between a kno-wn little city and a fictitious one,most of people would choose thenon-existing city.

In any case, there is informa-tion in ignorance (and probablyadvantages in forgetting).


Take the best

We often have to choose the best (buy a new car). The mostrational thing to do is to maximise a weighted score. The weightscan be extracted by past experiences.Example: a man with severe chest pain should be sent to thecoronary care unit or a regular nursing bed?.This method based resulted slow,and with a ' 50% efficiency.

A simpler decision tree is much moreeffective: first consider the mostimportant factor – had the patientexperienced hart attacks? If yes, goto intensive unit. Then the second:is the pain localized in chest? If yes,go to intensive unit, etc. etc.

This is why advertisers focus onirrelevant details for selling cars...


Darwin’s weighted decision


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 forsurviving. But in order to select a trait in this way, nature has toliterally kill everyone not carrying that trait before reproductive age.

A much less cruel but moreeffective selection is the sexualone.

In many species, just a tinyfraction of individuals (theleading male, for instance) doactually reproduce. In others,many have a chance ofreproducing, but someone ismore successful (bunga bunga!)


Sexual brain

Sexual selection is so effective,that a tiny improvement inattracting the opposite sex canresult in larger offspring.

This is the origin of theextreme sexual ornamentsfound in all sexually-reproducingspecies.

For humans, the principalornaments are (probably) powerand dexterity (mainlylinguistic): poetry, songs,...

It has been suggested that ourlarge brain (with art and alluseless brain products) is just asexual ornament.


Viral brain

One of the main social (andsexual) attractive character iscaptive story-telling (likejokes, hoaxes, epic novels...).

It is easy to recognize in mythsand hoaxes the “eigenvectors”of out mind (the “memes”).

For instance, an alreadyinteresting fact about a cannonthat launches chicken bodiesto test aircraft windowresistance, and is then rentedto test UK high-speed trainsbecomes ... the frozen chickenmyth.


Machiavellic brain

Monkey and ape societies are often complex social systems.

In such cases, the leadingposition is conquered by means ofalliances, not by pure muscle power.

This implies large cognitivepower, since one needs to elaboratenot only information about others,but also their mutual relationships.

Actually, the size of frontal cortex(the “monkey” brain) correlateswell with the group size (from whichone obtains the Dunbar number).


Logic brain

We find logic problems hard.

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

Red cards have an even digit on the back.


Social brain

But social tasks are easier...

How many situations should a policeman investigate (at minimum)to check if the following rule is violated?

People less than 18 cannot drink alcohol.


Social brain: 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 youwere the third person, up tohow much would you accept?

What is themost rational thing to do?


Social brain: 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 personallyknow this person and you knowthat in some future time he/shecan play you present role.You know that you’llnever meet again this person.You knowthat your choice will be madepublic in your school/office.

What is the most rational thing to do?


mechanisms: the Recognition project

The RECOGNITION project concerns new approaches forembedding self-awareness in ICT systems. This will be basedon the cognitive processes that the human species exhibits forself-awareness, seeking to exploit the fact that humans areultimately the fundamental basis for high performanceautonomic processes. This is due to the cognitive ability of thebrain to efficiently assert relevance (or irrelevance), extractknowledge and take appropriate decisions, when faced withpartial information and disparate stimuli.

http://www.recognition-project.eu


Perceptive dissonance

How many triangles are there?


Perceptive-cognitive dissonance

Name colors as fast as you can




BLUE YELLOW RED GREENGREEN RED YELLOW BLUERED GREEN BLUE YELLOW




BLUE YELLOW GREEN REDGREEN RED BLUE YELLOWRED GREEN YELLOW BLUE


Cognitive dissonance


Cognitive processes of information analysis

Timescales (reaction times)

Unconscious knowledge (perception and pre-attentive activations): fast(< 0.5 ms).Conscious knowledge (reasoning): medium (from seconds to hours).Learning/development: slow (from minutes to month).

Cost (Cognitive Economy Principle - amount of neural activation)

Unconscious knowledge: light (small and local activations).Conscious knowledge: heavy (large and diffused activations).Learning/development: very heavy (diffused activations).

Evolutionary Features (cognitive development)

Unconscious knowledge: critical period and “classical-Hebbian”learning only.Conscious knowledge: trial and error, observation/imitation andinduction learning.Learning/development: fixed hard-wired rules.


Schemes and heuristics

We denote with the term schemes the procedures that manageinformation and perform actions, and by heuristics themanagement of schemes (activation, modification, learning).

We classify schemes and heuristics in three modules: in the first onewe put the structures that deal with input, in the second the actualmanagement of information and actions and in the third thelearning.

This division is consistent with the the response times, but we thinkthat there is a common structure of heuristics and schemes


Schemes and heuristics


Other work in progress

with Andrea Guazzini, Duccio Fanelli, Timoteo Carletti, Pietro Lio,Emanuele Massaro, Alessandro Cini

Experiments/models on small groups dynamics.

Risk perception & epidemics.

Opinion dynamics (chaos).

Opinion formation.

Community detection.

At-the-device (smart phones) implementation of heuristics.

You may find some material on www.complexworld.net or you may writeto me [email protected] ([email protected])


searching for the second foundation

Education