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A second-order question...who is in a better position to judge whether a "real-world" situation is stable or uncertain? A human or a computer?
But the flu recency heuristic doesn’t seem that good for planning (e.g., resource procurement and distribution, staffing), which is why you need to make predictions. You would need more than a week in advance…
@Paul: I think GFT uses current-week search data, so it wouldn't add a whole lot to long-term planning. Also, you could use the heuristic current year == last year for longer-term prediction?
Ed(he/him) - PG Psychology
@Edwin, I think you're right, the law of recency doesn't require the previous week, just most recent data. So if you wanted annual predictions, you could use the previous year?
@Edwin. Good to know. The longer-term heuristic "could" still be better, but I wonder what the effect on the error is for heuristics v more complex models for the longer timeframe that matches planning timeframes needed. I don't see why it would necessarily be better.
Wouldn't that lead to similar problems as the Google approach. If the swine flu happened last year but doesn't affect this year then you overshoot. If the swine flu did not happen last year but happens this year you underestimate.
Obviously, if they're similar in performance - the simpler model is preferable anyway.
You could simply adjust the recency heuristic to need. E.g. on a 3-month basis or a 6-month basis. The longer the time frame you use as a reference, the smaller the influence from one-off events like the swine flu.
I don't get the heuristic advantage for the turkey illusion. Based on the most recent day/week approach, you make a tragically wrong prediction. Based on a more complex model (understanding feasts and other contextual info) you would make a better prediction...
Recency is only one heuristic. For the Turkey it could be something else. E.g. I as a newcomer look at the others besides me and see that there is not a single Turkey older than 6 months - so something is probably going to change - and I know it without too much complex data.
GOod point...I also started to think I missed something in the presentation of that point...
maybe referring to the evolutionary aspect of caution and fear being subverted by a bad predictive model
Could I ask a follow up? If you choose the heuristics based of some type of empirical methods would you not then risk some type of overfitting issues (assuming you are choosing from many heuristics)? But if you are worried about Knightean uncertainty, woudln't an analytic method of choosing heuristics fail - because the uncertainty by definition can't be quantified and included in a mathematical model?
I think the above is one of the more important results in the whole of machine learning!
Thank you :)
thank you -fascinating talk
thanks so much!
thanks Gerd, appreciated
Thanks very much - a wonderful talk
Thank you! Very interesting talk
Thank you Gerd!
Ed(he/him) - PG Psychology
Thank you Dr Gigerenzer!