#74 - Disagreeing about Belief, Probability, and Truth (w/ David Deutsch)
Oct 1, 2024
auto_awesome
David Deutsch, an influential thinker and author renowned for his contributions to the philosophy of science and quantum computation, takes center stage. He delves into whether belief is a useful lens for understanding cognition and debates the role of probability in meaningful analysis. The conversation challenges the limits of Bayesian reasoning, explores the complexities of truth, and critiques Popper's theories. Deutsch also reflects on the intricacies of language, creativity, and how they intersect with our understanding of reality.
The podcast critiques the concept of belief as a comprehensive lens for analyzing human thought processes, advocating for a focus on explanations instead.
It highlights the limitations of Bayesianism, which may oversimplify complex reasoning by relying on quantifying belief strength and can lead to erroneous conclusions.
The discussion emphasizes the challenges of predicting human behavior, arguing for nuanced theories that account for creativity and the dynamic nature of decision-making.
Deep dives
The Utility of Belief in Human Thought
Belief is considered by some thinkers as a misleading category when analyzing human thought processes. The speaker suggests that while belief can serve a purpose in specific contexts, such as religious faith discussions, it fails to accurately reflect how people engage with ideas in everyday situations. For instance, terms like 'I believe' may indicate varying levels of conviction that do not equate to a measurable belief strength. Instead, the speaker argues that thought and inquiry should be led by a focus on explanations rather than beliefs.
Critique of Bayesian Epistemology
The discussion critiques Bayesianism, highlighting its reliance on quantifying belief strength, which may lead to errors when applied to scientific reasoning or daily decision-making. The speaker points out paradoxes associated with Bayesian belief, such as intransitivity of support, which can distort conclusions drawn from hypotheses. By emphasizing the importance of explanations, the argument suggests that reliance on numerical beliefs may oversimplify complex thought processes. Thus, the speaker calls for a shift away from Bayesian quantifications towards a more explanatory framework in understanding human cognition.
Probability's Role in Theories of Knowledge
The speaker expresses skepticism towards the application of probability in both epistemology and physical theories, arguing that traditional uses fail to account for the complexity of knowledge acquisition. While recognizing the utility of probabilities in mathematical contexts, such as game theory, the speaker emphasizes that applying such probabilistic reasoning in real-world scenarios often leads to inaccurate predictions. The misuse of statistical data, as seen in prediction models, can lead to significant errors in judgment if the underlying explanations are not well-founded. This highlights a critical gap in relying on mere statistics without grounding them in robust explanatory frameworks.
The Challenge of Predicting Human Behavior
The conversation delves into the challenges of predicting human behavior, particularly in the context of economics and decision-making. While models of supply and demand attempt to forecast actions based on past behaviors, the speaker emphasizes that human actions are often unpredictable and influenced by innumerable factors. It is argued that a better approach to understanding human choice lies in employing nuanced theories that consider creativity and the dynamic nature of human response. Therefore, while predictions can offer insights, they must be approached with caution, recognizing the limitations and complexities involved.
Truth and the Correspondence Theory
The dialogue touches upon the correspondence theory of truth, focusing on the nuances of how knowledge claims relate to reality. The speaker acknowledges that while a perfect correspondence aligns with logical truth, language and expressions can often fall short of this ideal due to inherent ambiguities. By exploring the distinction between propositions as unambiguous entities and statements that may express vagueness, the speaker defends the notion that scientific inquiry is ultimately directed towards truth—even if that truth is never fully ascertainable. This offers a framework for understanding how we can pursue knowledge despite the fallibility of our expressions.
What do you do when one of your intellectual idols comes on the podcast? Bombard them with disagreements of course. We were thrilled to have David Deutsch on the podcast to discuss whether the concept of belief is a useful lens on human cognition, when probability and statistics should be deployed, and whether he disagrees with Karl Popper on abstractions, the truth, and nothing but the truth.
Follow David on Twitter (@DavidDeutschOxf) or find his website here.
We discuss
Whether belief is a fruitful lens through which to analyze ideas
Whether a non-quantitative form of belief can be defended
How does belief bottom out epistemologically?
Whether statistics and probability are useful
Where should statistics and probability be used in practice?
The Popper-Miller theorem
Statements vs propositions and their relevance for truth
Follow us on Twitter at @IncrementsPod, @BennyChugg, @VadenMasrani, @DavidDeutschOxf
Come join our discord server! DM us on twitter or send us an email to get a supersecret link
Believe in us and get exclusive bonus content by becoming a patreon subscriber here. Or give us one-time cash donations to help cover our lack of cash donations here.
