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GENESI E SVILUPPO DI SCOPERTE ED INVENZIONI RADICALI: TEORIA, CASI STUDIO ED APPLICAZIONI (Day 2) Ing. Francesco P. Appio Assegnista di Ricerca DESTEC.

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Presentation on theme: "GENESI E SVILUPPO DI SCOPERTE ED INVENZIONI RADICALI: TEORIA, CASI STUDIO ED APPLICAZIONI (Day 2) Ing. Francesco P. Appio Assegnista di Ricerca DESTEC."— Presentation transcript:

1 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

2 Agenda day 2 Deduction, Induction, Abduction Heuristics Intelligence and Intuition (and Instinct?) Paradigms and Socio-technical systems 2

3 PART 1 Deduction, Induction, Abduction 3

4 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

5 Examples Rule: If price increases, sales will decrease If A then B Case: We launched product A at a high price A Result: Product A’s sales were low Necessarily B 5 2. Case 3. Result 1. Rule

6 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. 6

7 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. 7

8 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

9 Examples Case: Increase price on Product AA Result: Sales declinedB Rule: Sales decline when price is too highIf A then probably B 9 2. Result 3. Rule 1. Case

10 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. 10

11 Characteristics of induction Induction is inconclusive in infinite time Hume ( ) 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." 11

12 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. 12

13 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) 13

14 Examples Result: Sales has gone down B Rule: Sales go down when prices is high If A then B Case: Check if price is too high for Product A Possibly A Rule 3. Case 1. Result

15 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. 15

16 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. 16

17 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. 17

18 PART 2 Heuristics 18

19 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 19

20 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 good- enough solution 20

21 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. 21

22 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? 22

23 PART 3 Intelligence and Intuition (and Instinct?) 23

24 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. 24

25 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. 25

26 … and instinct (that we have lost – Galimberti, 2009 ) 26 INTELLIGENCE INSTINCT INTUITION INTUITION TRAPPED IN INSTINCT INTUITION INTERPENETRATING INTELLIGENCE

27 PART 4 Socio-technical systems and paradigms 27

28 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. 28

29 Multi-level perspective on transitions 29

30 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. 30

31 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 31

32 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 32

33 (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 Gigerenzer, G., and Brighton, H. (2009). Homo heuristicus: why biased minds make better inferences. Topics in Cognitive Science 1, 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 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, Bergson, H. ( ), 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, 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, 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 33

34 End of day 2 … 34


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