Abstract
Gigerenzer and coauthors have described a remarkably fast and direct way of generating new theories that they term the tools-to-theories heuristic. Call it the TTT heuristic or simply TTT. TTT links established methods to new theories in an intimate way that challenges the traditional distinction of context of discovery and context of justification. It makes heavy use of rhetorical tropes such as metaphor. This chapter places the TTT heuristic in additional historical, philosophical, and scientific contexts, especially informational biology and digital physics, and further explores its strengths and weaknesses in relation to human limitations and scientific realism.
Thanks to Thomas Sturm and other members of the Department of Philosophy, Autonomous University of Barcelona; to Jordi Cat, Jutta Schickore, and the Indiana University Department of History and Philosophy of Science and Medicine, where I presented an early version of some of these ideas; and to Emiliano Ippoliti for the invitation to develop them at the Rome-Sapienza workshop, which travel problems prevented me from attending. I am indebted to Marco Buzzoni and to the Herbert Simon Society for the opportunity to present at the University of Macerata and in Turin, Italy, where Gerd Gigerenzer and Riccardo Viale made helpful comments. Thanks to David Danks for final improvements.
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Notes
- 1.
- 2.
Wider cultural orientations can also have a role. Gigerenzer and Goldstein (1996) note how different today’s concepts of computers and computation are from those of the era of the Jacquard loom in which Babbage worked. The attempt to understand current mysteries (such as “mind”) in terms of the latest technology, as culturally understood, may be a near universal human cultural tendency.
- 3.
Examples of data-driven discovery are Baconian induction and Pat Langley’s early BACON programs (Langley et al. 1987). Examples of theory-driven discovery are Einstein’s theories of relativity. By theory-driven, Gigerenzer also has in mind Popper’s “logic of discovery” via nonrational conjectures (Popper 1963).
- 4.
Note that this model incorporates a Popperian conjectures and refutation idea without explaining where the conjectures ultimately come from. In this respect, the model is regressive, for what are the established tools of the target system (the mind itself) for generating theory candidates, and where did they come from? Stated more generally, the regress point is that TTT reduces the problem of theory generation to the problem of method or tool generation, without resolving the latter. Sometimes theory suggests new methods and new instruments (see Sect. 8), a widely recognized sequence compared to the one Gigerenzer calls to our attention.
- 5.
Thanks to the U.S. National Institutes of Health for the graphics.
- 6.
Here the tool had a hardware dimension as well as a computational (software) dimension, immediately raising the question of whether (mostly unknown) biological brain organization could be construed as a digital computer. Analysts such as Fodor (1968 and after) were happy to assume that the brain was sufficiently complex to implement one or more digital computers, and hence that biological embodiment issues could simply be ignored. See Shapiro (2004) for criticism of the multiple realizability thesis.
- 7.
See also the special issue of Philosophy of Science 4 (1996) and Bishop and Downes (2002).
- 8.
- 9.
See my comment on Peirce in Sect. 8.
- 10.
Computability theory, molecular neuroscience, and the extended cognition movement make it more difficult than ever to say what counts as cognition.
- 11.
For other criticisms and clarifications see Artmann (2008), Collier (2008), Griffiths (2001), Griesemer (2005), Godfrey-Smith (2007), Godfrey-Smith and Sterelny (2016), Oyama (2000), Ridley (2000). Some informational biologists are attempting to develop richer conceptions of information, e.g., naturalized teleosemantic conceptions.
- 12.
But wait! Newell and Simon’s “physical symbol system hypothesis” states that a physical system, as they define it, “has the necessary and sufficient means for general intelligent action” (1976, 116). And Deutsch’s position (see below) is based on his claimed extension of the Church-Turing thesis to the physical universe (Deutsch 1985).
- 13.
It was Edward Fredkin of MIT and Carnegie Mellon University who originally reversed the abstraction hierarchy and said that information is more basic than matter and energy. Interestingly, biological theorist George Williams (1992) held that the informational gene is as basic as matter and energy.
- 14.
Objection! We can say that even an old-fashioned balance scale or spring scale can compute weights, so why not say that neurons in the brain compute visual outputs and that quantum physical processes compute the solutions to physical problems involving causal forces and laws of nature? Reply. The objection uses ‘compute’ in a very broad sense. For starters, we can make a distinction between those entities that employ a symbol system or code with symbol-transforming rules and those that do not.
- 15.
Deutsch (2011) presents himself as a strong realist who believes that his account is the eternal truth about the universe. In these passages, he seems to believe in cumulative truth against future revolution or even long-term, transformative evolution of scientific results. Yet, in other passages, he insists that scientific progress will continue for centuries. Moreover, he agrees with Popper (his favorite philosopher of science) that we cannot know now what we shall only know later. As an illustration, he even cites Michelson’s strong realist commitment to late 19th-century physics, a strong realism soon undermined by the relativity and quantum revolutions (Chap. 9). It is hard to see how transformative progress can continue if we already have the fundamental truth (Nickles 2017 and forthcoming a and b).
- 16.
The ontic interpretation of digital physics resurrects old questions of the sort raised by the Copenhagen interpretation of quantum mechanics and old questions involving hypothetical constructs, intervening variables, and postulatory theories. However, physicists have succeeded in making direct connections of therodynamics to information theory, for example. More controversially, the Landauer principle or limit establishes a lower bound energy cost for nonreversible computational processes such as erasure, Deutsch has provided a physicalist version of the Church-Turing principle, and cosmologists speak of a principle of conservation of information.
- 17.
This move is especially familiar in the social and behavioral sciences, where so much methodological ink has been spilled over the status of unobserved, hypothetical constructs versus intervening variables.
- 18.
A reverse process occurs when a once-established postulatory theory is demoted, deflated, to a mere tool, as has happened with Newtonian mechanics and several other historical successes. Here the move is from an intended, literal description of underlying reality to a mere “as if”; e.g., as if there were Newtonian forces in the world that acted instantaneously at a distance. The now-deflated theory is reduced to an instrument for calculation. Of course, well-established theories also serve as useful tools.
- 19.
- 20.
The positivists sometimes made the reverse move, insisting that universal causation and simplicity (for instance) were methodological principles rather than basic claims about the universe.
- 21.
No more than we can explain why (causally) a flagpole is 15 m high in terms of the length of its shadow and the angle to the sun.
- 22.
I am making a general point about mutual support and our account of it. I am not referring specifically to the bootstrap method of Glymour (1980).
- 23.
There is a distant connection here to what I have called “discoverability” as opposed to original discovery (Nickles 1985). Once a sufficiently rich body of domain information and investigative tools have been developed, by whatever means, it may then be possible to go back and rationally reconstruct a “discovery” process leading to the current theory. In this way, the theory is supported generatively (by rationally reasoning to it from prior premises, in part results made possible by having in hand that very theory), not merely consequentially (by testing predictions logically derived from it).
- 24.
One may sense a conflict here with Gigerenzer’s program of fast and frugal heuristics, which, as explicitly stated, include little or no domain knowledge (Gigerenzer et al. 1999). However, Gigerenzer’s ABC Group do not regard such heuristics as universal in scope. On the contrary, a major part of their research is to explore their limits of application, in terms of the structure of the task environment or domain involved (see Nickles 2016). The more that is known about the (statistical) structure of the environment, the better the chance of finding fast and frugal heuristics that work well in that context. In this sense, fast and frugal heuristics are very domain specific.
- 25.
For criticism of the Popper-Lakatos position on coupling, see Nickles (1987).
- 26.
This sort of coupling does provide resistance to the fast and frugal approach, which aims to apply minimal information, at least overtly. But against a rich background of information about the statistical structure of the environment, simple algorithms (as in heart attack triage) can work. On the other hand, this means that such fast and frugal methods are not as fast and frugal as they appear, except in post-discovery application.
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Nickles, T. (2018). TTT: A Fast Heuristic to New Theories?. In: Danks, D., Ippoliti, E. (eds) Building Theories. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-72787-5_9
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