GENESI E SVILUPPO DI SCOPERTE ED
INVENZIONI RADICALI:
TEORIA, CASI STUDIO ED APPLICAZIONI
(Day 2)
Ing. Francesco P. Appio
Assegnista di Ricerca
DESTEC
19 Marzo, 2015
Agenda day 2
• Deduction, Induction, Abduction
• Heuristics
• Intelligence and Intuition (and Instinct?)
• Paradigms and Socio-technical systems
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PART 1
Deduction, Induction, Abduction
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Deduction
In filosofia e in matematica, il processo logico nel quale, date certe
premesse e certe regole che ne garantiscono la correttezza, una
conclusione consegue come logicamente necessaria: in questo senso
sono forme di deduzione il sillogismo e la dimostrazione matematica.
(Treccani)
The inference of particular instances by reference to a general law or
principle. (Oxford Dict.)
4
Examples
Rule:
Case:
Result:
If price increases, sales will decrease
We launched product A at a high price
Product A’s sales were low
1.
Rule
If A then B
A
Necessarily B
2.
Case
3.
Result
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Characteristics of deduction
• The conclusion is true given the premises are true also
• All As are Bs.
• C is B.
• Therefore, C is A
• Deduction cannot lead to new knowledge
• … because the conclusion has already been embedded in the premise
• Human behaviors are rational.
• One of several options are more efficient in achieving the goal.
• A rational human will take the option which directs him to achieve his goal
• The above deductive inference simply provide examples that a rational man
will do rational things.
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Characteristics of deduction
• Usually inferences made with deductive methods do not specify
whether the premise is a necessary condition, a sufficient condition,
or both. For example, rationality is a necessary condition, but not a
sufficient condition, of making the correct choice. Sometimes people
may fail to select the right alternative because of lack of faith or
courage.
• Deduction relies on true premises
• It is fallible as we cannot logically prove all the premises are true.
• For Peirce (1931), deduction alone is a necessary condition, but not a
sufficient condition of knowledge.
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Induction
Procedimento logico, opposto a quello della deduzione, per cui
dall’osservazione di casi particolari si sale ad affermazioni universali (o,
nella statistica, alla formulazione delle regolarità statistiche): ragionare,
argomentare, dimostrare per induzione. Nelle scienze sperimentali, i.
empirica o incompleta, l’enunciazione di una legge valida in generale
soltanto sulla base di una successione finita di osservazioni, nel
presupposto che siano validi certi caratteri di regolarità nel fenomeno
studiato. (Treccani)
The inference of a general law from particular instances. (Oxf. Dict.)
8
Examples
Case:
Result:
Rule:
Increase price on Product A
Sales declined
Sales decline when price is too high
1.
Case
A
B
If A then probably B
2.
Result
3.
Rule
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Characteristics of induction
• Induction introduced by Francis Bacon is a direct revolt against
deduction. Bacon (1561/1626) found that deductive reasoners reply
on the authority of antiquity (premises made by masters)
• Inductive logic is based upon the notion that probability is the relative
frequency in long run and a general law can be concluded based on
numerous cases.
• A1, A2 A3 A100 are B.
• A1, A2, A3 ... A100 are C.
• Therefore, B is C.
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Characteristics of induction
• Induction is inconclusive in infinite time
• Hume (1711-1776) argued that things are inconclusive by induction because
in the infinite time there are always new cases and new evidence.
• We never know when a regression line will turn flat, go down or go up. Even
inductive reasoning using numerous accurate data and high power computing
can go wrong, because predictions are made only under certain specified
conditions.
• Induction is undefinable in a single case
• Induction generates empirical laws but not theoretical laws
• Induction cannot furnish us with new ideas because observations or sensory
data only lead us to superficial conclusions but not the "bottom of things."
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Characteristics of induction
• Induction is based on generality and law of large numbers.
• all empirical reasoning is essentially making inferences from a sample to a
population; the conclusion is merely probably (never certainly) true and
merely approximately (never exactly)
• Justifying this view with the Law of Large Numbers. On one hand, we don't
know the real probability due to our finite existence. However, given a large
number of cases, we can approximate the actual probability. We don't have to
know everything to know something. Also, we don't have to know every case
to get an approximation. This approximation is sufficient to fix our beliefs and
lead us to further inquiry.
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Abduction
Il sillogismo in cui la premessa maggiore è certa, mentre la premessa
minore è probabile, per cui anche la conclusione è soltanto probabile.
(Treccani)
Abduction or, as it is also often called, Inference to the Best Explanation
is a type of inference that assigns special status to explanatory
considerations. Most philosophers agree that this type of inference is
frequently employed, in some form or other, both in everyday and in
scientific reasoning. (Stanford Encyclopedia of Philosophy)
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Examples
Result:
Rule:
Case:
Sales has gone down
Sales go down when prices is high
Check if price is too high for Product A
1.
Result
B
If A then B
Possibly A
2.
Rule
3.
Case
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Characteristics of abduction
• It is not symbolic logic but critical thinking
• The surprising phenomenon X is observed
• Among hypotheses A, B, and C, A is capable of explaining X
• Hence, there is a reason to pursue A
• Abduction is hypothesis generation
• This process of inquiry can be well applied to exploratory data analysis: after
observing some surprising facts, we exploit them and check the predicted
values against the observed values and residuals. Although there may be
more than one convincing patterns, we "abduct" only those which are more
plausible. In other words, exploratory data analysis is not trying out
everything.
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Characteristics of abduction
• Abduction is not hasty judgment but proper categorization
• exploratory data analysis, as an applications of abduction, is not a permit for the
analyst to be naive to other research related to the investigated phenomena.
Progress in science depends on the observation of the right facts by minds furnished
with appropriate ideas. The intuitive judgment made by an intellectual is different
from that made by a high school student.
• In short, abduction by intuition, can be interpreted as observing the world
with appropriate categories which arise from the internal structure of
meanings. The implications of abduction for researchers is that the use of
exploratory data analysis is neither exhausting all possibilities nor making
hasty decisions. Researchers must be well-equipped with proper categories
in order to sort out the invariant features and patterns of phenomena.
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Conclusion part 1
Both deduction and induction have different merits and shortcomings.
For Peirce (1931) a researcher should apply abduction, deduction and
induction altogether in order to achieve a comprehensive inquiry.
Abduction and deduction are the conceptual understanding of a
phenomena, and induction is the quantitative verification. At the stage
of abduction, the goal is to explore the data, find out a pattern, and
suggest a plausible hypothesis with the use of proper categories;
deduction is to build a logical and testable hypothesis based upon
other plausible premises; and induction is the approximation towards
the truth in order to fix our beliefs for further inquiry. In short,
abduction creates, deduction explicates, and induction verifies.
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PART 2
Heuristics
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On heuristics (Gigerenzer and Brighton, 2009)
• Heuristics are efficient cognitive processes that ‘ignore’ information
• In contrast to the widely held view that less processing reduces
accuracy, the study of heuristics shows that less information,
computation, and time can in fact improve accuracy
• the discovery of less-is-more effects
• the study of the ecological rationality of heuristics
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On heuristics
• The term heuristic is of Greek origin, meaning ‘‘serving to find out or
discover.’’ The mathematician George Polya distinguished heuristics
from analytic methods; for instance, heuristics are indispensable for
finding a proof, whereas analysis is required to check a proof’s validity
• In the 1950s, Herbert Simon (1955), who studied with Polya in
Stanford, first proposed that people satisfice rather than maximize.
Maximization means optimization, the process of finding the best
solution for a problem, whereas satisficing means finding a goodenough solution
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The discovery of less is more
• Less-is-more effects: More information or computation can decrease
accuracy; therefore, minds rely on simple heuristics in order to be
more accurate than strategies that use more information and time.
• A less-is-more effect, however, means that minds would not gain anything
from relying on complex strategies
• They challenge the classical definition of rational decision-making as the
process of weighting and adding all information
• Note that the term less-is-more does not mean that the less
information one uses, the better the performance. Rather, it refers to
the existence of a point at which more information or computation
becomes detrimental, independent of costs.
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Ecological rationality
• All inductive processes, including heuristics, make bets. This is why a
heuristic is not inherently good or bad, or accurate or inaccurate, as is
sometimes believed. Its accuracy is always relative to the structure of the
environment.
• In which environments will a given heuristic succeed, and in which will it
fail?
• Stigmergy and lack of information (Grassé, 1959; Doyle and Marsh, 2013):
Worked guided by stimuli. Stigmergy can be defined as an indirect
coordination mechanism allowing autonomous individuals to structure
their collective activities through a shared local environment. Is perfect
information necessary? Practicable? Relevant?
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PART 3
Intelligence and Intuition (and Instinct?)
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Intelligence
The normal way our intelligence works is guided by needs and thus the
knowledge it gathers is not disinterested; it is relative knowledge. And
how it gathers knowledge is through what Bergson calls “analysis,” that
is, the dividing of things according to perspectives taken.
Comprehensive analytic knowledge then consists in reconstruction or
re-composition of a thing by means of synthesizing the perspectives.
This synthesis, while helping us satisfy needs, never gives us the thing
itself; it only gives us a general concept of things.
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Intuition
Bergsonian intuition then consists in entering into the thing, rather
than going around it from the outside. This “entering into,” for Bergson,
gives us absolute knowledge.
Because intuition in Bergson is “integral experience”, it is made up of
an indefinite series of acts.
The first act is a kind of leap, opposed to the idea of a re-constitution
after analysis. One should make the effort to reverse the habitual mode
of intelligence and set oneself up immediately in the duration. But
then, second, one should make the effort to dilate one's duration into a
continuous heterogeneity. Third, one should make the effort to
differentiate the extremes of this heterogeneity.
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… and instinct (that we have lost – Galimberti, 2009)
INSTINCT
INTUITION TRAPPED IN
INSTINCT
INTELLIGENCE
INTUITION
INTUITION INTERPENETRATING
INTELLIGENCE
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PART 4
Socio-technical systems and paradigms
27
Socio-technical systems
The sociotechnical landscape is the wider context, which influences
niche and regime dynamics. It is a landscape in the literal sense,
something around us that we can travel through; and in a metaphorical
sense, something that we are part of, that sustains us. It includes
spatial structures (e.g. urban layouts), political ideologies, societal
values, beliefs, concerns, the media landscape and macro-economic
trends. The socio-technical landscape represents the greatest degree of
structuration in the sense of being beyond the control of individual
actors.
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Multi-level perspective on transitions
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Thought collectives/Thought styles (Fleck, 1935)
• Thought collective: a community of persons mutually exchanging
ideas or maintaining intellectual interaction.
• Thought style: the readiness for directed perception, with
corresponding mental and objective assimilation of what has been so
perceived.
Structure of a thought collective: esoteric and exoteric circles.
The role of proto-ideas.
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Paradigms (Kuhn, 1962)
La conoscenza non è un processo cumulativo; ci sono perdite e guadagni.
Non abbiamo un progresso verso la verità con teorie sempre più generali.
Abbiamo invece un progresso a partire da stadi precedenti (progresso
retrospettivo a partire da, non verso qualcosa.
Un paradigma:
• stabilisce quale genere di cose esistono nell’universo
• determina anche quale genere di domande possono essere legittimamente
poste
• determina anche le tecniche d’indagine appropriate e cosa conta come
evidenza empirica
• determina anche cosa conta come soluzione di un problema
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Paradigms (Kuhn, 1962)
Una caratteristica di un paradigma è anche il modo con cui è accettato dagli
scienziati (dogma?).
Il ruolo della conoscenza tacita e dei rompicapo.
I fenomeni possono essere valutati in maniera diversa perché gli scienziati
fanno riferimento a valori diversi: egli sembrerebbe dire che ogni paradigma
crea i propri fatti, i propri fenomeni. E quindi in realtà non c’è neanche una
sovrapposizione parziale. L’idea di Kuhn è che le teorie sono
incommensurabili, cioè che non possono essere comparate in maniera
completa perché creano modelli di esperienza diversi
• Incommensurabilità delle osservazioni (Gestalt)
• Incommensurabilità del significato dei termini descrittivi
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(Incomplete) reading list
Chong, H. Y. (1994). Abduction? Deduction? Induction? Is there a logic of exploratory data analysis?
Proceedings of the annual meeting of the American education research association. Online
http://files.eric.ed.gov/fulltext/ED376173.pdf
Gigerenzer, G., and Brighton, H. (2009). Homo heuristicus: why biased minds make better inferences. Topics in
Cognitive Science 1, 107-143.
Kuhn, T. (1962). The Structure of Scientific Revolution. University of Chicago Press, Chicago, IL.
Fleck, L. (1935). The Genesis and Development of a Scientific Fact, translated in 1979. University of Chicago
Press, Chicago, IL.
Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level
perspective and a case-study. Research Policy 31, 1257-1274.
Bergson, H. (1903-1923), La pensée et le mouvant. Essai set Conférences, Quadrige Grand Textes, Puf.
Simon, H. (1955). A behavioral model of rational choice. Quarterly Journal of Economics 69, 99-118.
Grassé, P. P. (1959). La Reconstruction du Nid et les Coordinations Inter-individuelles chez Bellicosoitermes
Natalensis et Cubitermes. La Théorie de la Stigmergie: Essai d’Interprétation du Comportement des Termites
Constructeurs. Insectes Sociaux, 6, 41-81.
Doyle, M. J., and Marsh, L. (2013). Stigergy 3.0: from ants to economies. Cognitive Systems Research 21, 1-6.
Peirce, C.S., (1931). Collected Papers. Vol. 1. Harvard University Press, Boston, MA
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End of day 2 …
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