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. Energetica
and CSDC, Universit` di Firenze
a

(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 and
Fall of the Roman Empire (Edward
Gibbon), the cycle spans about 500 years of
history in a far future.
The story begins when Hari Seldon, a
mathematician, proves the theoretical
possibility to predict the future of a society
on a mathematical basis.
After various vicissitudes, Hari manages to
establish two foundations, at the “opposite
ends of the galaxy”. The goal is that of
shortening the period of chaos after the
expected fall of the Galactic Empire from the
estimated 30,000 years to only 1,000 years.

The Foundations
The two foundations have very diﬀerent tasks. The ﬁrst is asked to
preserve the technical knowledge, and is destined to dominate the
nearby planets (and then the whole ex-empire).
The Second Foundation instead must remain secret. It is formed by
mathematicians, who have the task of writing the equations which
model in detail the future of the humanity, and correcting the
“deviations”.
Of course not all goes smoothly. The First
Foundation must face some Seldon crisis
(bifurcation points?), and they succeed with the
secret help of the Second Foundation, which has
developed psychological methods to manipulate
people.
There is also a war of the First Foundation against
the Second, because the former do not want to be
manipulated by the latter.

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

FuturICT will build a sophisticated simulation, visualization and
participation platform, called the living earth platform
( planetary-scale data collection and simulations). This
platform will power crisis observatories, to detect and mitigate
crises, and participatory platforms, to support the
decision-making of policy-makers, business people and citizens,
and to facilitate a better social, economic and political
participation.

The psycohistory

The fundamental intuition of Hari Seldon is that the various planets
can be considered uncorrelated.
Asimov was a chemist, and in fact this
approximation is the basis of “chemical” equations
(mass law). He says explicity: psycohistory is like
the gas law for humans.
But we know that microscopic symmetries and
constraints reﬂect on macroscopic behavior.
And as happens in chemistry, Asimov-Seldon
recommends to apply these equations only to very
large populations (and to keep this practice
secret!).
So, the main question is: which is the simplest
model 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 computational
demanding. We can discriminate the “usage” of cognitive
capabilities by fMRI and response times. For instance, a good
ping-pong player never “thinks” to the next move.
We cannot avoid unconscious
knowledge. Some partially “blind”
people (blind sight) can detect
movements even if they cannot
“understand” what they see.
Human recognition needs emotional
components, otherwise the subjects
cannot even recognise themselves in
a mirror.


Heuristics as weak intelligence

We have to take a lot of decisions in
everyday life.
Generally, these decision are
satisfactory, but we all experience
frustration for having chosen the bad
choice, or having been cheated.
Twerski and Kahneman pointed out
the 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.


Examples of classic heuristics: anchoring

When taking a decision, we heavily rely on just one piece of
information (the one easier to recall), and only in a second moment
we “adjust” the answer according to other factors.
A classical example is the question Estimate the probability of
death by lung cancer and by vehicle accidents.
People tends to assign a higher
probability to car accidents (since
they are much more commonly
reported by press) but lung
cancer causes about 3 times
more deaths than cars.


Representativeness

Although we always assume a probabilistic world, we are insensitive to
prior probability of outcomes. We ignore preexisting distribution of
categories or base rate frequencies. Bayes’ theorem is not easily
understood.
We are insensitive to sample size. We tend to draw strong inferences
from small number of cases
We have a misconception of chance:
gambler’s fallacy. We think that
chance will “correct” a series of “rare”
events.
We have a misconception of regression.
We deny chance as a factor causing
extreme 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 to
bet on the 6 or on any other number?
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 ne-
gatives) is positive for the defendant.
There are no other cues. Which is
the probability that the defendant is
guilty?


Heuristics as fast and frugal processing

At present, heuristics have a better : they can be considered as
optimized methods of saving computational resourced and giving
faster answers (Gigerenzer).
Many everyday problems would require unbounded rationality to be
solved, and a large time for samplig all possibilities.
But we do not try every possible partner when choosing a mate (nor
a tiny fraction of them...).
In a variable world, sometimes the “rules of
thumb” are really better then the weighted
methods taught by economists.
In real world, with redundant information,
Bayes’ theorem and “rational” algorithms
quickly become mathematically complex
and computationally intractable.


Recognition heuristics

In 1991 Gigerenzer and Goldstein asked students in California and
Germany to estimate whether S. Diego or S. Antonio had a larger
population. German students were much more accurate, simply
because most of them did not know S. Antonio.
The same test was performed on soccer outcome, ﬁnancial
estimates, etc.
But Oppenheim (2003) showed Which town has a larger
that we use also other cues. If population?
asked to judge between a kno- A B
wn little city and a ﬁctitious one, Erfurt Witten
most of people would choose the Trier Duisburg
non-existing city. Bochum Neuss
In any case, there is informa- Krefeld Leipzig
tion in ignorance (and probably Darmstadt Mannheim
advantages in forgetting). Cottbus Rostock


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. The weights
can be extracted by past experiences.
Example: a man with severe chest pain should be sent to the
coronary care unit or a regular nursing bed?.
This method based resulted slow,
and with a 50% efficiency.
A simpler decision tree is much more
effective: first consider the most
important factor – had the patient
experienced hart attacks? If yes, go
to intensive unit. Then the second:
is the pain localized in chest? If yes,
go to intensive unit, etc. etc.
This is why advertisers focus on
irrelevant 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 for
surviving. But in order to select a trait in this way, nature has to
literally kill everyone 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. In others,
many have a chance of
reproducing, but someone is
more successful (bunga bunga!)


Sexual brain
Sexual selection is so eﬀective,
that a tiny improvement in
attracting the opposite sex can
result in larger oﬀspring.
This is the origin of the
extreme sexual ornaments
found in all sexually-reproducing
species.
For humans, the principal
ornaments are (probably) power
and dexterity (mainly
linguistic): poetry, songs,...
It has been suggested that our
large brain (with art and all
useless brain products) is just a
sexual ornament.

Viral brain

One of the main social (and
sexual) attractive character is
captive story-telling (like
jokes, hoaxes, epic novels...).
It is easy to recognize in myths
and hoaxes the “eigenvectors”
of out mind (the “memes”).
For instance, an already
interesting fact about a cannon
that launches chicken bodies
to test aircraft window
resistance, and is then rented
to test UK high-speed trains
becomes ... the frozen chicken
myth.

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
not only information about others,
but also their mutual relationships.
Actually, the size of frontal cortex
(the “monkey” brain) correlates
well with the group size (from which
one 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 many
dollars you will oﬀer to a third person. He/she can accept and you
share the money, or he/she can refuse and in this case both of you
will 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?


Social brain: The dictator game

This is the same as the ultimatum, but in this case the third person
cannot 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 future time 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.
What is the most rational thing to do?


mechanisms: the Recognition project
The RECOGNITION project concerns new approaches for
embedding self-awareness in ICT systems. This will be based
on the cognitive processes that the human species exhibits for
self-awareness, seeking to exploit the fact that humans are
ultimately the fundamental basis for high performance
autonomic processes. This is due to the cognitive ability of the
brain to eﬃciently assert relevance (or irrelevance), extract
knowledge and take appropriate decisions, when faced with
partial 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 GREEN
GREEN RED YELLOW BLUE
RED GREEN BLUE YELLOW




BLUE YELLOW GREEN RED
GREEN RED BLUE YELLOW
RED 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 and
induction learning.
Learning/development: fixed hard-wired rules.


Schemes and heuristics

We denote with the term schemes the procedures that manage
information and perform actions, and by heuristics the
management of schemes (activation, modiﬁcation, learning).
We classify schemes and heuristics in three modules: in the ﬁrst one
we put the structures that deal with input, in the second the actual
management of information and actions and in the third the
learning.
This division is consistent with the the response times, but we think
that there is a common structure of heuristics and schemes


Schemes and heuristics


Other work in progress

with Andrea Guazzini, Duccio Fanelli, Timoteo Carletti, Pietro Li´,
o
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 write
to me franco.bagnoli@uniﬁ.it (franco.bagnoli@complexworld.net)


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