Noisy decisions and the perils of flexibility

Posted on Mon 18 March 2024 in Research • Tagged with dynamic programming, noisy choices

Consider the following: (i) sequential setting: a person who sequentially engages in costly search among alternatives and upon stopping receives the highest value seen so far, and (ii) parallel setting: a person who ex-ante decides the number of alternatives to examine, and then selects the highest value among them.

Obviously …


Continue reading

Do we search more with parallel search or with sequential search?

Posted on Fri 01 March 2024 in Research • Tagged with search, prototyping

In their study examining the tradeoffs between parallel and sequential testing, Loch et al (2001) highlight a rather curious conclusion: sequential testing, because of its ability learn from one to next, will result in greater number of tests. Indeed, the ability to learn from one to next can enable faster …


Continue reading

How many people should I ask for advice (part 3)?

Posted on Thu 07 December 2023 in Research • Tagged with search, dynamic programming

The earlier model assumed that we wish to maximize the expected value from the decision (“impact”). What if we are interested in maximizing the probability that we are making the right decision. How would the optimal choice change? As before, after asking \(n\) people, we will update the true value …


Continue reading

How many people should I ask for advice (part 2)?

Posted on Sat 02 December 2023 in Research • Tagged with search, dynamic programming

The earlier musings calculated the expected value under the assumption that we need to decide how many people to ask upfront. Now suppose we can ask people sequentially. That is, we ask one person, and depending on their report can decide to stop or to ask more people, and so …


Continue reading