Discuss with Dobelli – part 1

Chapter by chapter.

1. Survivorship bias is only an example of a deeper problem, namely reasoning based on data sampling. Reason based on highly uncertain data is likely uncertain as well. Additionally, making decisions based on incomplete data is crucial, but not making decisions is also making them- postponing changes conditions, in which decisions are made.

2. Network means team-development and involves synergy between team members. Learning alone used to be almost impossible. Now, however, networks are built in the web and we are “less” alone (more “connected”).

3. We have powerful causality-driven models and keep them updated after we conduct new experiments.

4. In multidimensional incomplete knowledge settings error propagation is severe. When making decisions, keep a tighter range of arguments and prune your decision tree slightly farther.

5. Sunk cost fallacy is problematic — we shall not only keep in mind odds, but also implied odds as well as the change of the conditions under which decisions are made. So, it is not about “sunk costs”, but odds.

6. Reciprocity is a matter of perspective. Different sides of equations quantify their perspectives differently. Still, overall, it seems reasonable to say that providing others with tools that help them solve their problems helps you a lot (and should help). Nevertheless, even though problems are solved using a truthful value-sharing mechanism, the mechanism is context-driven and (from current human perspective) slowly updated.

7/8. Confirmation bias is an emergent property of learning. We learn our model our reality and use it in real-time to understand what is happening. We have many pieces of evidence that our model works, so updating it requires thorough verification of experiment data. Still, given how our models of reality are, we should be inclined to challenge them quite often.

9. Authority is an authority for a reason. If we verify their opinion on their topic related to their expertise, we must be authorities as well. Still, if we consider to verify their opinion on a topic unrelated to their expertise, they are not an authority (in this area). As there is much knowledge  and our current models cover only certain aspects of it (mathematics, physics, etc.), authorities are confined to rel. tiny aspects of knowledge. Still, those aspects are built upon much evidence. The rest of this argument can be found in 7/8.

10. The contrast effect is only partially about comparing. It is due to incorrect estimation of value of components used in arguments. As our resolution of perception is limited to a rel. tiny area, i.e. we notice only the “big” things and only see “differences” (and not absolute values), seeing “big differences” (requires comparing) attracts our attention. Still, if we were able to see the quality of arguments better, comparing between uninformative factors would not impress us.


About misha

Imagine a story that one can't believe. Hi. Life changes here. Small things only.
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