"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya, "How to Solve It", 1945)
"The aim of heuristics is to study the methods and rules of discovery and invention. [...] Heuristic, as an adjective, means 'serving to discover'." (George Pólya, "How to Solve It", 1945)
"Heuristic (it is of Greek origin) means discovery. Heuristic methods are based on experience, rational ideas, and rules of thumb. Heuristics are based more on common sense than on mathematics. Heuristics are useful, for example, when the optimal solution needs an exhaustive search that is not realistic in terms of time. In principle, a heuristic does not guarantee the best solution, but a heuristic solution can provide a tremendous shortcut in cost and time." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"A heuristic is ecologically rational to the degree that it is adapted to the structure of an environment. Thus, simple heuristics and environmental structure can both work hand in hand to provide a realistic alternative to the ideal of optimization, whether unbounded or constrained." (Gerd Gigerenzer & Peter M Todd, "Fast and Frugal Heuristics: The Adaptive Toolbox" [in "Simple Heuristics That Make Us Smart"], 1999)
"Fast and frugal heuristics employ a minimum of time, knowledge, and computation to make adaptive choices in real environments. They can be used to solve problems of sequential search through objects or options, as in satisficing. They can also be used to make choices between simultaneously available objects, where the search for information (in the form of cues, features, consequences, etc.) about the possible options must be limited, rather than the search for the options themselves. Fast and frugal heuristics limit their search of objects or information using easily computable stopping rules, and they make their choices with easily computable decision rules." (Gerd Gigerenzer & Peter M Todd, "Fast and Frugal Heuristics: The Adaptive Toolbox" [in "Simple Heuristics That Make Us Smart"], 1999)
"In the language of mental models, such past experience provided the default assumptions necessary to fill the gaps in the emerging and necessarily incomplete framework of a relativistic theory of gravitation. It was precisely the nature of these default assumptions that allowed them to be discarded again in the light of novel information - provided, for instance, by the further elaboration of the mathematical formalism - without, however, having to abandon the underlying mental models which could thus continue to function as heuristic orientations." (Jürgen Renn, "Before the Riemann Tensor: The Emergence of Einstein’s Double Strategy", [in "The Universe of General Relativity"] 2000)
"Theories of choice are at best approximate and incomplete. One reason for this pessimistic assessment is that choice is a constructive and contingent process. When faced with a complex problem, people employ a variety of heuristic procedures in order to simplify the representation and the evaluation of prospects. These procedures include computational shortcuts and editing operations, such as eliminating common components and discarding nonessential differences. The heuristics of choice do not readily lend themselves to formal analysis because their application depends on the formulation of the problem, the method of elicitation, and the context of choice." (Amos Tversky & Daniel Kahneman, "Advances in Prospect Theory: Cumulative Representation of Uncertainty" [in "Choices, Values, and Frames"], 2000)
"Heuristics are rules of thumb that help constrain the problem in certain ways (in other words they help you to avoid falling back on blind trial and error), but they don't guarantee that you will find a solution. Heuristics are often contrasted with algorithms that will guarantee that you find a solution - it may take forever, but if the problem is algorithmic you will get there. However, heuristics are also algorithms." (S Ian Robertson, "Problem Solving", 2001)
"Models of bounded rationality describe how a judgement or decision is reached (that is, the heuristic processes or proximal mechanisms) rather than merely the outcome of the decision, and they describe the class of environments in which these heuristics will succeed or fail." (Gerd Gigerenzer & Reinhard Selten [Eds., "Bounded Rationality: The Adaptive Toolbox", 2001)
"Mental shortcuts, also called heuristic simplifications,
help us analyze situations and make decisions quickly in our daily life.
However, this process often leads us astray when analyzing decisions with risk
and uncertainty. Because investing decisions involve substantial risk and
uncertainty, our decisions are biased in predictable ways. The
representativeness bias causes us to extrapolate the past and assume that good
companies are good investments. The familiarity bias causes us to believe that
firms we are familiar with are better investments than unfamiliar firms. Thus,
we own more local firms and our employer’s stock and few international stocks.
Thus, these biases lead to low diversification and higher risks."
"Psychological research has shown that the brain uses
shortcuts to reduce the complexity of analyzing information. Psychologists call
these heuristic simplifications. These mental shortcuts allow the brain to
generate an estimate of an answer before fully digesting all the available
information. Two examples of shortcuts are known as representativeness and
familiarity. Using these shortcuts allows the brain to organize and quickly
process large amounts of information. However, these shortcuts also make it
hard for investors to analyze new information correctly and can lead to
inaccurate conclusions." (John R Nofsinger, "The Psychology of Investing", 2002)
"Most people give substantial weight to anecdotal evidence, perhaps so much that it will cancel out positive recommendations found in consumer reports. People's tendency to give undue weight to some types of information is called the availability heuristic. A heuristic is a rule of thumb, a mental shortcut." (Barry Schwartz, "The Paradox of Choice: Why More Is Less", 2004)
"A heuristic is defined as a simple rule that exploits both evolved abilities to act fast and struc- tures of the environment to act accurately and frugally. The complexity and uncertainty of an environment cannot be determined independently of the actor. What matters is the degree of complexity and uncertainty encountered by the decision maker." (Christoph Engel & Gerd Gigerenzer, "Law and Heuristics: An interdisciplinary venture" [in "Heuristics and the Law", 2006)
"Heuristics are needed in situations where the world does not permit optimization. For many real-world problems (as opposed to optimization-tuned textbook problems), optimal solutions are unknown because the problems are computationally intractable or poorly defined." (Christoph Engel & Gerd Gigerenzer, "Law and Heuristics: An interdisciplinary venture" [in "Heuristics and the Law", 2006)
"A heuristic is a rule applied to an existing solution represented in a perspective that generates a new (and hopefully better) solution or a new set of possible solutions." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)
"A second class of metaphors - mathematical algorithms, heuristics, and models - brings us closer to the world of computer science programs, simulations, and approximations of the brain and its cognitive processes." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)
"A heuristic is a decision rule that utilizes a subset of the information set. Since in virtually all cases people must economize and cannot analyze all contingencies, we use heuristics without even realizing it." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)
"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)
"When heuristics don’t yield the results we expect, you’d
think we would eventually realize that something’s wrong. Even if we don’t
locate the biases, we should be able to see the discrepancy between what we
wanted and what we got, right? Well, not necessarily. As it turns out, we have
biases that support our biases! If we’re partial to one option - perhaps because
it’s more memorable, or framed to minimize loss, or seemingly consistent with a
promising pattern - we tend to search for information that will justify choosing
that option. On the one hand, it’s sensible to make choices that we can defend
with data and a list of reasons. On the other hand, if we’re not careful, we’re
likely to conduct an imbalanced analysis, falling prey to a cluster of errors
collectively known as 'confirmation biases'." (Sheena Iyengar, "The Art of
Choosing", 2010)
"In particular, the accurate intuitions of experts are better
explained by the effects of prolonged practice than by heuristics. We can now
draw a richer and more balanced picture, in which skill and heuristics are
alternative sources of intuitive judgments and choices."
"This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)
"Heuristics are an evolutionary solution to an ongoing
problem: we have limited mental resources. As such, they have a very long and
thoroughly time-tested history of helping us - on average - make better decisions." (Peter
H Diamandis, "Abundance: The Future is Better Than You Think", 2012)
"Heuristics are simplified rules of thumb that make things simple and easy to implement. But their main advantage is that the user knows that they are not perfect, just expedient, and is therefore less fooled by their powers. They become dangerous when we forget that." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)
"Mental models represent possibilities, and the theory of mental models postulates three systems of mental processes underlying inference: (0) the construction of an intensional representation of a premise’s meaning – a process guided by a parser; (1) the building of an initial mental model from the intension, and the drawing of a conclusion based on heuristics and the model; and (2) on some occasions, the search for alternative models, such as a counterexample in which the conclusion is false. System 0 is linguistic, and it may be autonomous. System 1 is rapid and prone to systematic errors, because it makes no use of a working memory for intermediate results. System 2 has access to working memory, and so it can carry out recursive processes, such as the construction of alternative models." (Sangeet Khemlania & P.N. Johnson-Laird, "The processes of inference", Argument and Computation, 2012)
"The art of reasoned persuasion is an iterative, recursive
heuristic, meaning that we must go back and forth between the facts and the
rules until we have a good fit. We cannot see the facts properly until we know
what framework to place them into, and we cannot determine what framework to
place them into until we see the basic contours of the facts." (Joel
P Trachtman, "The Tools of Argument",
2013)
"A good heuristic decision is made by 1) knowing what to look
for, 2) knowing when enough information is enough (the 'threshold of
decision' ), and 3) knowing what decision to make." (Patrick Van Horne, "Left of
Bang", 2014)
"A rule of thumb, or heuristic, enables us to make a decision fast, without much searching for information, but nevertheless with high accuracy. [...] A heuristic can be safer and more accurate than a calculation, and the same heuristic can underlie both conscious and unconscious decisions." (Gerd Gigerenzer, "Risk Savvy: How to make good decisions", 2014)
"Even if virtual worlds were tabula rasa, we are encumbered with a great deal of cognitive baggage. Our brains are hardwired with many mental shortcuts to help us make sense of the world. We simply do not have the time to carefully process every piece of information that comes our way. To cope with this inundation of information, our brains have developed automated heuristics that filter and preprocess this information for us. Thus, when we encounter new media and technological devices, we fall back on the existing rules and norms we know." (Nick Yee, "The Proteus Paradox", 2014)
"A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods." (Gerd Gigerenzer et al, "Simply Rational: Decision Making in the Real World", 2015)
"Because economists go through a similar training and share a
common method of analysis, they act very much like a guild. The models
themselves may be the product of analysis, reflection, and observation, but
practitioners’ views about the real world develop much more heuristically, as a
by-product of informal conversations and socialization among themselves. This
kind of echo chamber easily produces overconfidence - in the received wisdom or
the model of the day. Meanwhile, the guild mentality renders the profession
insular and immune to outside criticism. The models may have problems, but only
card-carrying members of the profession are allowed to say so. The objections
of outsiders are discounted because they do not understand the models. The
profession values smarts over judgment, being interesting over being right - so
its fads and fashions do not always self-correct." (Dani Rodrik, "Economics
Rules: The Rights and Wrongs of the Dismal Science", 2015)
"Heuristic decision making is fast and frugal and is often
based on the evaluation of one or two salient bits of information." (Amitav
Chakravarti, "Why People (Don’t) Buy: The Go and Stop Signals", 2015)
"Probability theory is not the only tool for rationality. In situations of uncertainty, as opposed to risk, simple heuristics can lead to more accurate judgments, in addition to being faster and more frugal. Under uncertainty, optimal solutions do not exist (except in hindsight) and, by definition, cannot be calculated. Thus, it is illusory to model the mind as a general optimizer, Bayesian or otherwise. Rather, the goal is to achieve satisficing solutions, such as meeting an aspiration level or coming out ahead of a competitor." (Gerd Gigerenzer et al, "Simply Rational: Decision Making in the Real World", 2015)
"Judgments made in difficult circumstances can be based on a limited number of simple, rapidly-arrived-at rules ('heuristics'), rather than formal, extensive algorithmic calculus and programs. Often, even complex problems can be solved quickly and accurately using such 'quick and dirty' heuristics. However, equally often, such heuristics can be beset by systematic errors or biases." (Jérôme Boutang & Michel De Lara, "The Biased Mind", 2016)
"A heuristic is a strategy we derive from previous experience with a similar problem." (Darius Foroux, "Think Straight", 2017)
"In any analysis of any part of the world, it is mandatory to institute a general reasoning in which the whole - the Absolute - is also included. This is what science scrupulously avoids. Science is all about the parts, and ignoring the whole. Science is non-holistic, which is why it cannot arrive at a grand unified, final theory of everything. From the whole you can get to every part, because the whole defines the parts. If you start with the parts, as science does, you can never get to the whole because the parts are necessarily defined piecemeal, heuristically and with no regard to the whole, since the whole is unknown. A bottom-up approach can never work. Only top-down approaches have any chance of working. Empiricists are always parts people and bottom-up people. Rationalists are holistic and top-down. These are opposite worldviews. The PSR is an explanatory, top-down principle. Randomness is a non-explanatory, bottom-up speculation." (Thomas Stark, "God Is Mathematics: The Proofs of the Eternal Existence of Mathematics", 2018)
"The social world that humans have made for themselves is so complex that the mind simplifies the world by using heuristics, customs, and habits, and by making models or assumptions about how things generally work (the ‘causal structure of the world’). And because people rely upon (and are invested in) these mental models, they usually prefer that they remain uncontested." (Dr James Brennan, "Psychological Adjustment to Illness and Injury", West of England Medical Journal Vol. 117 (2), 2018)
"We used the word 'heuristics' to describe aspects of
software development that tip toward the liberal arts. Its counterpart, 'algorithms', was its alter ego on the technical side. Heuristics and
algorithms are like two sides of the same coin. Both are specific procedures
for making software do what it does: taking input, applying an operation, and
producing output. Yet each had a different purpose." (Ken Kocienda, "Creative
Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs", 2018)
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