No matter if you ponder what stocks to invest in, wonder whom to marry or what to eat, or if you decide which employee to hire: our daily life is full of decisions. We study how people and other organisms make decisions and how they ought to make these decisions in order to behave adaptively.... read more

Industry Collaborations and Exchanges

We are involved and interested in industry collaborations and exchanges to apply decision making research to business problems. If you are interested in a collaboration and/or an exchange of knowledge and experience, please contact us. 


Previous collaborations and exchanges included:

-financial investment

-automobile industry

-publishing industry

-insurance business

-internet start-up

Some interesting insights about decision making and cognition

Cognitive Psychologist John R. Anderson gets the 2011 Franklin Award in Computer and Cognitive Science for his ACT theory.


Ecological Rationality: Fast and Frugal Heuristics


Herbert Simon: the father of bounded rationality

There is an alternative to optimization and irrationality. A couple of thousand journal articles before the heuristics-and-biases tradition became popular, Herbert Simon, stressed that optimization is rarely possible in the real world, and thus a theory of rationality needs to study how people make decisions when optimization is out of reach (Simon 1956). Instead of relying on unrealistic optimization models and striving to compute optimal solutions for a given task, people use simple strategies, seeking solutions that are good enough with respect to an organism’s goals (=satisficing). He also stressed that behavior and performance result from both cognition and an organism’s environment: “Human rational behavior … is shaped by a scissors whose two blades are the structure of task environments and the computational capabilities of the actor” (Simon 1990, p. 7)


Simon also argues that memory acts as an extension of the environment in which human thought processes take place:

“We can think of the memory as a large encyclopaedia or library, the information stored by topics (nodes), liberally cross-referenced (associational links), and with an elaborate index (recognition capability) that gives direct access through multiple entries to the topics. Longterm memory operates like a second environment, parallel to the environment sensed through eyes and ears, through which the problem solver can search and to whose contents he can respond.” (Simon 1996, p. 88)

Gerd Gigerenzer, Director at the Max Planck Institute for Human Development and Director of the Harding Center for Risk Literacy in Berlin on Heuristics:


Cognitive psychologist Douglas L. Hintzman explains the importance of the precision with which psychological models are defined (and not how they are labeled):


“Explanation...demands a theory...that predicts effects of the manipulated variables on performance of each task. Crude distinctions between ‘‘systems’’ are seldom sufficient for this purpose. Further, once a sufficiently elaborate process model is in hand, it is not clear that the notion of a system is any longer of much use. Once the model has been spelled out, it makes little difference whether its components are called systems, modules, processes, or something else; the explanatory burden is carried by the nature of the proposed mechanisms and their interactions, not by what they are called." (Hintzman 1990, p. 121)

The computer science and cognitive psychology researcher Allen Newell points out the importance of concepts and the pitfalls when focusing on binary oppositions:


“Psychology also attempts to conceptualize what it is doing, as a guide to investigating these phenomena. How do we do that? Mostly, so i seems to me, by the construction of oppositions—usually binary ones. We worry about nature versus nurture, about central versus peripheral, about serial versus parallel, and so on.

To bring this point home, I give in [the following] a list of oppositions that have currency in psychology. These issues, I claim—about whether one or the other characterizes or explains some phenomenon—serve to drive a large part of the experimental endeavor . There are, to be sure, a few strands of theory of a different stripe, where the theory strives for some kind of quantitative explanation over a class of phenomena, parametrically expressed. I do not wish to deny these studies; neither do they dominate the current style of research enough to quiet my concern.

I stand by my assertion that the two constructs that drive our current experimental style are (1) at a low level, the discovery and empirical exploration of phenomena […]; and (2) at the middle level, the formulation of questions to be put to nature that center on the resolution of binary oppositions. At the high level of grand theory, we may be driven by quite general concerns: to explore development; to discover how language is used; to show that man is a processor of information; to show he is solely analysable in terms of contigencies of reinforcement responded to. But it is through the mediation of these lower two levels that we generate our actual experiment and give our actual explanations. Indeed, psychology with its penchant for being explicit about its methodology has created special terms, such as “orienting attitudes” and “pretheoretical dispositions,” to convey the large distance that separates the highest levels of theory from the immediate decisions of day to day science.


Binary Oppositions:

  1. Nature versus nurture
  2. Peripheral versus central
  3. Continuous versus all-or-none learning
  4. Uniprocess versus duoprocess learning (Harlow)
  5. Single memory versus dual memory (STM-LTM) (Melton)
  6. Massed versus distributed practice
  7. Serial versus parallel processing
  8. Exhaustive versus self-terminating search
  9. Spatial logic versus deep structure
  10. Analog versus digital
  11. Single code versus multiple codes
  12. Contextual versus independent interpretation
  13. Trace decay versus continuous development
  14. Stages versus continuous development
  15. Innate versus learned grammars (Chomsky)
  16. Existence versus non-existence of latent learning
  17. Existence versus non-existence of subliminal perception
  18. Grammars versus associations for language (reality of grammar)
  19. Conscious versus unconscious
  20. Channels versus categorizing in auditory perception (Broadbent)
  21. Features versus templates
  22. Motor versus pure perception in perceptual learning
  23. Learning on non-error trials versus learning on error trials
  24. Preattentive versus attentive" 

     (Newell 1973, p.287)


Arguments for developing more integrative systems are also provided by A. Newell (1990):


A single system (mind) produces all aspects of behavior. It is one mind that minds them all. Even if the mind has parts, modules, components, or whatever, they all mesh together to produce behavior. Any bit of behavior has causal tendrils that extend back through large parts of the total cognitive system before grounding in the environmental situation of some earlier times. If a theory covers only one part or component, it flirts with trouble from the start. It goes without saying that there are dissociations, independencies, impenetrabilities, and modularities. These all help to break the web of each bit of behavior being shaped by an unlimited set of antecedents. So they are important to understand and help to make that theory simple enough to use. But they don’t remove the necessity of a theory that provides the total picture and explains the role of the parts and why they exist. (pp. 17-18)

Featured Publications:


Marewski, J. N., & Schooler, L. J. (2011). Cognitive Niches: An ecological model of strategy selection. Psychological Review, 118, 393-437.

Marewski, J. N., & ΨMehlhorn, K. (2011). Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making, 6, 439-519. [Lead article; equal contribution of all authors.]

Marewski, J. N., Pohl, R.F., & Vitouch, O. (2011). Recognition-based judgments and decisions: What we have learned (so far). Judgment and Decision Making, 6, 359–380.

Marewski, J. N. (2010). On the theoretical precision, and strategy selection problem of a single-strategy approach: A comment on Glöckner, Betsch, and Schindler. Journal of Behavioral Decision Making, 23, 463-467.

Marewski, J. N., Gaissmaier, W., & Gigerenzer, G. (2010). Good judgments do not require complex cognition. Cognitive Processing, 11, 103-121. [Equal contribution of J.N.M. and W.G.; Article among the 10 most downloaded articles in this journal in 2011.]

Marewski, J. N., Gaissmaier, W., Schooler, L. J., Goldstein, D. G., & Gigerenzer, G. (2010). From Recognition to Decisions: Extending and Testing Recognition-Based Models for Multi-Alternative Inference. Psychonomic Bulletin & Review, 17, 287-309

Pachur, T., Bröder, A., & Marewski, J. N. (2008). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue? Journal of Behavioral Decision Making, 21, 183–210.



  • Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129-138.
  • Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1-20.
  • Simon, H. A. (1996). The sciences of the artificial (3rd ed). Cambridge, MA: MIT Press.
  • Hintzman, D. L. (1990). Human learning and memory: connections and dissociations. Annual Review of Psychology, 41, 109-139.
  • Newell, A. (1973). You can't play 20 questions with nature and win: Projective comments on the papers of this symposium. In W. G. Chase (Ed.), Visual information processing. New York: Academic Press.
  • Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.