Thinking and deciding
"Thinking and Deciding has established itself as a required text and important reference work for students and scholars of human judgments, decisions, and rationality. In this, the third edition, Jonathan Baron delves further into many of the key questions addressed in the previous editions. For example, how should we think? What, if anything, keeps us from thinking that way? How can we improve our thinking and decision making? Baron has also revised or expanded his treatment of topics such as risk, utilitarianism, Bayes's theorem, utility measurement, decision analysis and values. By emphasizing decision making, Baron has made Thinking and Deciding, Third Edition, more relevant to researchers in applied fields, such as medicine, business, public policy, and law, while maintaining its appeal to graduate and undergraduate students."--Jacket.
Stats for nerds
6recommend
14mentions
8people
2008–2026years
79% recommend21% neutral
Mentions per year
What Hacker News says
- The points you raise are indeed wilful ignorance, easily rebutted.
I gave you a link which summarizes the subject well, with plenty of references. If you're genuinely interested in understanding the world, you would study that. But what you're doing instead is cherry-picking reasons to refuse to accept strong evidence. Al Gore, really? You're really scraping the bottom of the denial barrel.
Ironically, in your attempts to not be conned by grifters, you're falling for one of the biggest cons of all: the ongoing oil company attacks on anything that threatens the oil industry. If you're not getting paid by them, you're being suckered.
Aside from the reference I gave, you would also benefit from reading books like "Asking the Right Questions", by Neil Browne & Stuart Keeley; Carl Sagan's "The Demon Haunted World"; and "Thinking and Deciding" by Jonathan Baron. These will help you distinguish between rational thinking and rationalizations.
> So, spare me your shame game identity politics.
Nothing I've said even remotely approaches anything like that. I'm describing well-documented and well-supported facts, and pointing out that refusing to try to understand or accept those is problematic, and that by doing so along with millions of others, you're perpetuating major problems in the world. But your victimhood mentality fits right in with the conspiratorial mindset.
- This is a philosophical argument.
The way to look at this is first to pin down what we mean when we say Human Commonsense Reasoning (https://en.wikipedia.org/wiki/Commonsense_reasoning). Obviously this is quite nebulous and cannot be defined precisely but OG AI researchers have a done a lot to identify and formalize subsets of Human Reasoning so that it can be automated by languages/machines.
See the section Successes in automated commonsense reasoning in the above wikipedia page - https://en.wikipedia.org/wiki/Commonsense_reasoning#Successe...
Prolog implements a language to logically interpret only within a formalized subset of human reasoning mentioned above. Now note that all our scientific advances have come from our ability to formalize and thus automate what was previously only heuristics. Thus if i were to move more of real-world heuristics (which is what a lot of human reasoning consists of) into some formal model then Prolog (or say LLMs) can be made to better reason about it.
See the paper Commonsense Reasoning in Prolog for some approaches - https://dl.acm.org/doi/10.1145/322917.322939
Note however the paper beautifully states at the end;
Prolog itself is all form and no content and contains no knowledge. All the tasks, such as choosing a vocabulary of symbols to represent concepts and formulating appropriate sentences to represent knowledge, are left to the users and are obviously domain-dependent. ... For each particular application, it will be necessary to provide some domain-dependent information to guide the program writing. This is true for any formal languages. Knowledge is power. Any formalism provides us with no help in identifying the right concepts and knowledge in the first place.
So Real-World Knowledge encoded into a formalism can be reasoned about by Prolog. LLMs claim to do the same on unstructured/non-formalized data which is untenable. A machine cannot do "magic" but can only interpret formalized/structured data according to some rules. Note that the set of rules can be dynamically increased by ML but ultimately they are just rules which interact with one another in unpredictable ways. Now you can see where Prolog might be useful with LLMs. You can impose structure on the view of the World seen by the LLM and also force it to confine itself only to the reasoning it can do within this world-view by asking it to do predominantly Prolog-like reasoning but you don't turn the LLM into just a Prolog interpreter. We don't know how it interacts with other heuristics/formal reasoning parts (eg. reinforcement learning) of LLMs but does seem to give better predictable and more correct output. This can then be iterated upon to get a final acceptable result.
PS: You might find the book Thinking and Deciding by Jonathan Baron useful for background knowledge - https://www.cambridge.org/highereducation/books/thinking-and...
- It is commonsense; all decisions are not in the same category.
If you need specific references, Nassim Taleb's books are a good place to start. His book The Black Swan: The Impact of the Highly Improbable divides events into two categories; Extremistan and Mediocristan. Events/Decisions in the former can have extreme unpredictable non-linear effects while those in the latter are more sedate and have predictable cumulative effects. An example is the earning capacity of A Dentist (steady regular income with a predictable statistical model i.e. Mediocristan) vs. A Venture Capitalist (unpredictable, large risks, all his bets might fail or one may become a unicorn i.e. Extremistan).
For a comprehensive study of decision-making, see the excellent Thinking and Deciding by Jonathan Baron.
- >No, the authors of the article seem to be writing from personal experience and have used an interesting analogy to describe the solution in somewhat geometric terms (a “watershed”). If there were some mathematical way to describe the wiring of the neurons of someone making good decisions maybe they would have used that instead. Absent such a concept they resorted to metaphor instead. Such is the burden of philosophers.
The "Watershed Metaphor" doesn't make any sense at all. You can contrive anything and call it "Creativity"/"Philosophizing" etc. but that doesn't make it correct.
>the skill of decision making—while being creative—as this: it’s the ability to navigate a many dimensional design space
This is just a platitude and can mean anything and everything.
>Normal engineering has been reduced to a series of unidimensional logical decisions. This is my constraint…these are my material properties (or whatever) and I shall optimize for cost. Easy peasy. A computer could do it. This is the realm of building codes and strict industry standards.
WHAT ??? Nobody who knows the "Scientific Method" and has trained as an "Engineer" will ever agree with you. You are taking "one facet" of a specific Engineering Discipline and generalizing it to the whole Domain of "Engineering". This is a fundamental logical fallacy.
>But to be creative, you need to navigate a large search space of ideas. Every decision you take opens up new pathways and poses new challenges to solve. What is the best path through such a space? Copy someone else’s design and call yourself an innovator? Stick to first principles? Develop your intuition and then use it to guide your choices? Iterate often and fast? Being creative involves a completely different mindset for working a decision making…and I think it’s different because navigating a large dimensional idea space requires more than mere logic. This is what distinguishes creative people from mere mortals. And decision making in such a space has to be different too.
This is mere verbiage. See Baron's Thinking and Deciding for a detailed study of the relevant subjects.
>That was why the article was so interesting. One day there might be a mathematical way to define this problem and solution properly. Until then, like philosophers of old, they will have to resort to metaphor to explain what they have grasped with their minds. Some people will get it and some will not. But it’s not nonsense business-speak
You setup your own points and traced a path through it; means Nothing.
- This article suffers from the same shortcomings like many others on the subject (including the books quoted by the author) i.e. trying to reduce the Decision-Making process into a prescriptive bullet point list. This methodology doesn't work.
IMO, the "correct" way to study it is through Gary Klein's Recognition Primed Decision Model (RPDM) (https://en.wikipedia.org/wiki/Recognition_primed_decision) and the Naturalistic Decision Making (NDM) Framework (https://en.wikipedia.org/wiki/Naturalistic_decision-making).
For a comprehensive overview of various approaches/models see Thinking and Deciding by Jonathan Baron and The Psychology of Judgment and Decision Making by Scott Plous.
- 1) Sources of Power: How people make decisions by Gary Klein
Introduces https://en.wikipedia.org/wiki/Recognition_primed_decision It really resonated with me.
2) Thinking and Deciding by Jonathan Baron. Quite thorough and comprehensive.
3) There are also books on "TRIZ"(https://en.wikipedia.org/wiki/TRIZ) a technique for problem solving, which arose from the former Soviet Union.
Related
Behavioral Game Theory
2 recommend · 3
Mindware
2 recommend · 2
The psychology of judgment and decision making
4 recommend · 4
Knowledge and decisions
4 recommend · 9
Heuristics and biases
5 recommend · 16
The decision book
4 recommend · 6
The thinker's toolkit
recommend · 7
