Sure. If you've studied stochastic processes like Brownian motion, check out the derivation starting with the last paragraph on p.4 of this note: https://drive.

Sure. If you've studied stochastic processes like Brownian motion, check out the derivation starting with the last paragraph on p.4 of this note: https://drive.google.com/file/d/1tQj4ZGja6jKADJGiFj-dcQAirWoUFX46/view?usp=sharing https://drive.google.com/file/d/1tQj4ZGja6jKADJGiFj-dcQAirWo... If that's foreign to you, you might be interested in this write-up I did about election-forecasting in which I considered the same example, just in discrete time rather than continuous time: http://nautil.us/issue/70/variables/how-to-improve-political-forecasts http://nautil.us/issue/70/variables/how-to-improve-political... The basic idea is that if you increase the volatility of a random-walk process, say by making the step-sizes larger, that won't actually make the probability of finishing above where you started any less (or more) volatile. The higher volatility means that from any given starting point your final resting place is more dispersed, but you're also more likely to range farther from home as you go. The two effects exactly cancel. Taleb's critique misses the second part of that.