Reviews (14)

It's not the technique, it's the logic behind it
Most evolutionary computation or math books deal with the techniques of solving problems. This book teachs you how to think of a solution for the problem you face, and not what problems are appropriate for the technique in hand.
improve your problem solving ability
The authors have updated their successful first edition, though the latter, printed in 99, was scarcely obsolete. A heuristic can be basically a rule of thumb, dressed up in fancier language. What the authors intend is for you to develop an intuition about when to use modern algorithms. Where is almost every case, these are actually implemented on a computer; a reflection of the cheap availability of computing power to most readers.
get this book!, read it!, understand it! :)
i have not finished reading this book, but it's 'worth it' if only for the first two chapters! :) anyone interested in dynamical systems (control aspects), general problem solving, AI, and human thinking should read and understand this book! :) work the problems! :) think! enjoy! :) ... Read more

Subjects:  1. Computer Books: General    2. Computer Science    3. Computers    4. Heuristic    5. Linear Programming    6. Mathematical recreations    7. Mathematics    8. Problem solving    9. Algorithms & procedures    10. Computers / Computer Science   

Reviews (4)

Our biases over-ride our rational appraisal. An important volume from Kahneman and Tversky.
In this volume Daniel Kahneman and the late Amos Tversky gathered together 35 authoritative papers that demonstrate through well-designed experiments and through observation the hard-wired biases and heuristics that influence (or define) the way humans go about making choices when the outcomes are from certain.
Prospect theory is a subset of Keynes'stheory of probability
The essays contained in this book show convincingly that the standard decision theoretic model taught world wide since the mid 1940's,the subjective expected utility model based on the subjective approach to probability of Ramsey,De Finetti,and Savage,is not supported by the experimental evidence.The essays successfully show how the use of Prospect Theory accounts for the underweighting(subadditive-subproportional) and overweighting(superadditive-superproportional)of decision weights(non linear"probabilities").Three basic judgmental heuristic operations are preformed by decision makers in the real world.These operations are intuitive and based on the perceptions of the decision maker.The first heuristic that decision makers use in making probability evaluations is called representativeness.Judgments of probability are based on what is perceived as similar.The second heuristic is the availability heuristic.It,like the third heuristic,specifies that decision makers concentrate only on that evidence,upon which the probabilities will be estimated,that is most easily obtained or is immediately available.The third heuristic is called the anchoring heuristic.Decision makers use only that evidence that comes first.Tversky and Kahneman,as well as all of the other essay authors,argue that their experimental evidence demonstrates or shows that decision makers do not understand the mathematical laws of probability(additivity of probabilities,addition principle,multiplication principle,marginal probability,conditional probability,joint probability).They also do not understand basic statistical concepts(regression to the mean of a probability distribution).In 1921,in his A Treatise on Probability(TP),J M Keynes pointed out that the purely mathematical conception of probability was a very small subset of what he called the logical theory of probability.In order to apply the purely mathematical laws of probability correctly,a decision maker had to have a complete sample space of all possible outcomes specified in advance.An equivalent assumption is that the decision maker knows for certain what the particular probability distribution is.Secondly,probability preferences would have to be specified by a complete order that was linear or proportional.Any decision situation that did not satisfy these conditions had a weight of evidence less than one.Keynes specified a variable,w,called the weight of the evidence,that measured the completeness of the relevant,potential evidence that was available to the decision maker.It was defined on the unit interval between 0 and 1,just like Ellsberg's rho variable that would serve as a measure of the ambiguity of the evidence.The existence of ambiguity automaticaly will lead to violations of the purely mathematical laws of probability.Contrary to Kahneman and Tversky,Ellsberg,like Keynes before him,argued that these calculations are not erroneous and the decision makers are not irrational or biased.The claims made by Tversky, Kahneman and their many followers(Shiller,for example),that the subjects in their experiments are probabilistically and statistically illiterate,makes no sense because the problems that are presented to the experimental subjects do not allow the subjects to unambiguously define a unique probability distribution or a complete sample space of all possible outcomes(someexamples are the blue-green taxi cab problem,the rare Asian disease problem,the battlefield problem,the Linda-bankteller problem,and the lawyer-engineer problem).Let us now turn to the representativeness heuristic.The representativeness heuristic turns out to be none other than Keynes's degree of similarity or likeness or resemblance discussed by Keynes in chapter 3 and Part III of the TP.The anchoring and availability heuristics are identical to the statement that the weight of the evidence is less than 1 for a real world decision maker.Keynes showed that decision makers would usually be able to use interval estimates(upper-lower probabilities) only.You automatically will violate the mathematical laws of probability,which only hold in the limiting case where w=1,given linear probability preferences.Keynes also showed this in his examples of his conventional coefficient of weight and risk,c.The answers obtained when one applies the c coefficient will be sub and super additive.Keynes ,however,would argue that these are not biases or errors,but correct calculations obtained with incomplete information.The vast majority of decision makers are attempting to reason probabilistically without the benefit of knowing a unique probability distribution,a complete sample space,or being able to specify a complete order over all outcomes.They are rational.Tversky and Kahneman are requiring "SUPERRATIONALITY".Every calculation of a probability estimate that does not have a weight of 1 or violates Carnap's rule of total evidence will violate the mathematical laws of probability.L J Cohen,repeating Keynes's argument,spent 20 years trying to get this point across to Kahneman and Tversky inthe journal Brain and Behavioral Science(1975-1994) .Tversky's support theory is a belated attempt to remedy their omission,but it has not been successfuly integrated into Prospect theory(1979)or Cumulative Prospect theory(1992),where the weighting function is stilla function of a single variable representing probability,although additional parameters have been incorporated into the model.One could argue that it is Tversky and Kahneman who are irrationally insisting that decision makers use the mathematical laws of probability in situations where those laws are not applicable.

my humble opinion
Of course, my humble opinion relative to Nobel award committee will hold little intrinsic value, other than a layman's interpretation and application.Read more

Subjects:  1. Cognitive Psychology    2. Heuristic    3. Judgment    4. Psychology    5. Cognition & cognitive psychology    6. Psychology & Psychiatry / Cognitive Psychology   

Reviews (24)

Challenge what you know about OOA/D
I have been studying the object oriented methodology for some time now. I felt that I had a good understanding of what OOP was all about. I have studied OOA/D and design patterns from numerous sources. All of my sense for OOA/D knowledge changed completely when I read this book. This book really showed me that I was stuck somewhere in the middle of the paradigm shift between action oriented programming (aka procedural programming) and object oriented programming. After reading this, I feel like my knowledge in OOA/D has truly advanced to the next level.
I've been searching for good books on design patterns...
I have plenty of books on patterns, but I want to get more information on the basics of OOP and why the patterns are effective instead of "this is pattern X, and this what it does." This is a very complicated subject, which I have no doubt that most programmers on the planet have very little understanding of. We mostly write crummy code that "works", and most of the time that's good enough.
Must Read for programmers,designers,architects
The book gives you succinct,simple,common sense(which is really uncommon) guidelines on OO design.
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Subjects:  1. Computers    2. Computers - Languages / Programming    3. Object-Oriented Programming    4. Object-oriented programming (C    5. Object-oriented programming (Computer science)    6. Programming - Object Oriented Programming    7. Science/Mathematics    8. Computer aided design (CAD)    9. Computers / Programming / Object Oriented    10. Object-oriented programming (OOP)   

Reviews (6)

Worthwhile Insight into Mental Shortcuts
People aren't computers. Human beings live in a real world of scarcity and constraint. Even though time and information may be scarce, human beings must make high-stakes decisions. Probability and logic offer models for the thought process of choosing between alternatives, but decision makers often do not have enough hours, data and skill to use these sophisticated approaches. Fortunately, some rough and ready cognitive shortcuts perform as well as or better than the most elaborately sophisticated models - at least in the real world context of limited information and time. Working with the ABC Research Group, authors Gerd Gigerenzer and Peter M. Todd explore some of those shortcuts, called "heuristics." They discuss in length and depth a series of experiments that demonstrate the value of heuristics. This is not light reading. It requires a level of comfort with academic style, mathematics and symbolic logic. Readers unfamiliar with cognition literature may find it a struggle - but we believes that those who persevere will find enough new insight to make the effort worthwhile.

Gigerenzer's clearesttext - very inspiring.
Gerd Gigerenzer is probably THE guru in heuristics - the art of picking a few shorthand rules to help us make complex decisions - but I found other works of his (Adaptive Thinking, Bounded Rationality) pretty dry going. By contrast this volume - a collection of stimulating, sometimes provocative essays by members of the ABC Research group (the centrer for Adaptive Behavior and Cognition) explores many facets of Heuristics and even puts heuristics to the test against other more computationally complex techniques to pick the outcomes for several complicated problems. Time and again, the simple rules of thumb out-perform the grindingly thorough statistical routines. For anyone researching human behaviour, the implicit challenge is to stop asking huge batteries of questions - and to look instead for the little telltale rules by which people navigate their complicated environments.
Well, i liked it anyway
Whether you like a book depends on what information you're looking for. i make computer models of human behavior so this book, which is easy to read but filled with concrete solutions and lots of supporting dat, was near-perfect for meRead more

Subjects:  1. Artificial Intelligence - General    2. Cognitive Psychology    3. Logic    4. Psychology    5. Artificial intelligence    6. Cognition & cognitive psychology    7. Cognitive theory    8. Economics    9. Psychology & Psychiatry / Cognitive Psychology    10. Psychology | Cognitive    11. Psychology & Psychiatry   

Reviews (1)

Of vast importance
This collection of articles has its origin in the work of one of the editors (Daniel Kahneman) and Amos Tversky (now deceased) in the 1970's. The first article in the book gives an introduction to this work and a brief historical survey. This work, along with current developments, is extremely important, for it sheds light on the differences (if any) between "intuitive judgment" and judgment that is based on more quantitative, mathematical, or algorithmic reasoning. If human judgment in uncertain environments is based on a limited number of simplifying heuristics, and not on extensive algorithmic processing, this would be very important for someone who is attempting to implement or simulate human reasoning in a machine. Economics, finance, and political decision-making are other areas that need a more accurate view of human judgment. Indeed, the "rational agent" assumption in classical economics, wherein the person makes choices by assessing the probability of each possible outcome and then assigning a utility to each, is considered to be fundamental, even axiomatic. It is therefore of great interest to examine challenges to this assumption.
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Subjects:  1. Cognitive Psychology    2. Critical thinking    3. Judgment    4. Psychology    5. Reasoning (Psychology)    6. Philosophy & theory of psychology    7. Psychology & Psychiatry / General   

Reviews (3)

A Good Guide to The Field
The Second Edition of "Information Retrieval", by Grossman and Frieder is one of the best books you can find as a introductory guide to the field, being well fit for a undergraduate or graduate course on the topic. It is somewhat a parallel to "Modern Information Retrieval", by Baeza-Yates and Ribeiro-Neto.
Great reference and textbook for IR practitioners and students
Information Retrieval is a textbook for computer science students and a reference book for IR practitioners. The References section is an excellent starting point for conducting literature research.
Extremely Clear "Fundamentals" Book
If you're working in the IR industry, or want to develop software in this field, this book is a great starting point. A clarification: this will is not a book for researchers -- instead think of it as a book for advanced practitioners or engineers needing to work in this area. Inside you'll see complete worked examples of several fundamental computations rather than detailed proofs. ... Read more

Subjects:  1. Computer Books: General    2. Computer Science    3. Computers    4. Computers - General Information    5. General    6. Natural Language Processing    7. Computers / Computer Science    8. Computers / General    9. Computers-Natural Language Processing    10. General Theory of Computing    11. Mathematics-General    12. Natural language & machine translation   

Subjects:  1. Heuristic    2. Problem solving    3. Psychology    4. Research    5. Research & Methodology    6. Science    7. Social Science / Research    8. Social research & statistics   

Reviews (1)

Good Book for Advanced Computing
Algorithmics for Hard Problems addresses an important (if somewhat small) group of technical people who work exclusively on intractable problems. It takes a great deal of creativity and a large set of intellectual tools to make even modest progress on these problems. Prof. Hromkovic's book presents and organizes a big package of ideas and tools for approaching these problems. Even the last section, entitled "A Guide to Solving Hard Problems" addresses some of the practical issues of working on problems for which a completely satisfactory answer may be out of reach.

Subjects:  1. Computational complexity    2. Computer Books: General    3. Computer Science    4. Computers    5. Computers - General Information    6. Logic    7. Networking - General    8. Parallel Processing    9. Parallel processing (Electroni    10. Parallel processing (Electronic computers)    11. Programming - Parallel Programming    12. Theory Of Computing    13. Algorithms & procedures    14. Boolean circuits    15. Boolean functions    16. Complexity    17. Computers / Computer Science    18. Heuristics    19. Randomized and Approximation Algorithms    20. VLSI circuits    21. communication complexity    22. formal languages    23. parallel computations   

Reviews (4)

A Book of Research, not Ideas
This book is academic and a dry read. There are better books out there which address this same material that makes for an easier read. But if you want to read the original work that paved the road for those books and usability now, here it is. Academic research that is usability's foundation!

Very satisfied
I bought this book for class and aced the class. I found this book helpfull and inspiring for people in the web usability field of study. Even though this is 1994 book, web usability aticles apply to today world as well.

Disappointing
I do usability inspections as part of my job.I bought this book based on Nielsen's reputation and the description of this book here on Amazon.com.I was very disappointed to find that this book was scholarly, not practical.After scanning the book, I returned it for refund. ... Read more

Subjects:  1. Computer Books: General    2. Computers - General Information    3. Human-Computer Interaction    4. Programming - General    5. User Interface    6. User interfaces (Computer syst    7. User interfaces (Computer systems)    8. Computers / Programming / General    9. Software engineering   

Reviews (1)

A Brilliant, Readable Introduction to the Logic of Sociology
This is a beautifully written, clear and understandable analysis of the diverse ways of thinking in contemporary sociology.It is written to be understandable by undergraduates who are serious about the discipline and are about to undertake research on their own.Graduate students will find it indispensible as they make very important decisions about their own careers.Reading Abbott is much easier than trying to figure these things out on your own .The author's concept of "fractual heuristics" is an innovative and insightful way of thinking about the interaction of different kinds of ideas in the discipline.There is too much herefor me to summarize in a short review, but it is well worth reading.Read more

Subjects:  1. Heuristic    2. Methodology    3. Philosophy    4. Research    5. Social Science    6. Social sciences    7. Sociology    8. POLITICS & GOVERNMENT   

Subjects:  1. Artificial Intelligence - General    2. Computer Bks - General Information    3. Computer Books: General    4. Computer Science    5. Computers    6. Computers - General Information    7. Logic Design    8. Adaptive Algorithms    9. Algorithms & procedures    10. Computers / Logic Design    11. Dynamic Systems    12. Evolutionary Algorithms    13. Evolutionary Programming    14. Fitness Landscapes    15. Genetic Algorithms    16. Heuristics    17. Immune Systems    18. Optimization    19. Problem Solving    20. Systems Evolution   

Subjects:  1. Heuristic    2. Methodology    3. Politics / Current Events    4. Public Policy - Social Services & Welfare    5. Research    6. Social Science    7. Social Services & Welfare    8. Social service    9. Sociology    10. Behavioural theory (Behaviourism)    11. Political Science / Social Services & Welfare    12. Social research & statistics    13. Social, group or collective psychology   

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Science    4. Computers    5. Computers - General Information    6. Information Technology    7. Linear Programming    8. Algorithm design    9. Computational intelligence    10. Computer experiments    11. Computers / Computer Science    12. Computing and Information Technology    13. Evolutionary computation    14. Global optimization    15. Heuristics    16. Metaheuristics    17. Statistical methods   

Subjects:  1. Computer Science    2. Engineering - Electrical & Electronic    3. Heuristic programming    4. Management    5. Science/Mathematics    6. Technology    7. Technology & Industrial Arts    8. Telecommunication (Engineering)    9. Telecommunication systems    10. Telecommunications    11. Theory Of Computing    12. Communications engineering / telecommunications    13. Technology / Telecommunications   

Subjects:  1. General    2. Jurisprudence    3. Law    4. Legal Reference / Law Profession    5. Jurisprudence & General Issues    6. Law / General   

Subjects:  1. Computational complexity    2. Computers    3. Computers - Communications / Networking    4. Data Processing - Parallel Processing    5. Networking - General    6. Parallel Processing    7. Parallel processing (Electroni    8. Parallel processing (Electronic computers)    9. Programming - General    10. Reference - General    11. Science/Mathematics    12. Theory Of Computing    13. Boolean circuits    14. Boolean functions    15. Boolesche Funktionen    16. Boolesche Schaltkreise    17. Complexity    18. Computer Communications & Networking    19. Computers / Computer Science    20. Computers / Programming / General    21. Computers-Networking - General    22. Computers-Reference - General    23. Formale Sprachen    24. Heuristics    25. Kommunikationskomplexität    26. Mathematical theory of computation    27. Parallele Berechnungen    28. Randomized and Approximation Algorithms    29. VLSI circuits   

Subjects:  1. Computer algorithms    2. Engineering - General    3. Fuzzy sets    4. General    5. Heuristic programming    6. Mathematical Analysis    7. Mathematical optimization    8. Mathematics    9. Programming - Algorithms    10. Science/Mathematics    11. Set Theory    12. Mathematics / Applied    13. Optimization   

Subjects:  1. Artificial Intelligence    2. Computer Bks - General Information    3. Data processing    4. General    5. Heuristic programming    6. Operations Research (Engineering)    7. Operations research    8. Problem solving    9. Questions & Answers    10. History of ideas, intellectual history   

Subjects:  1. Algorithms    2. Applied    3. Arithmetic    4. Combinatorial optimization    5. General    6. Heuristic programming    7. Linear Programming    8. Mathematics    9. Science/Mathematics    10. Combinatorics & graph theory    11. Mathematics / Advanced    12. Optimization   

Subjects:  1. Artificial Intelligence    2. Expert systems (Computer scien    3. MYCIN (Computer system)    4. Questions & Answers    5. Reference