Re: problem 1: Jelly bean number estimates are just like thermometer readings, except that the reading is in someone’s head, rather than their hand. So the obvious answer is to average everyone’s initial, solitary impressions, absent reason to expect one individual or another is an above-average (or below-average) estimator.

If your friends use lopsided weighting schemes in their second answers, should you re-update? This depends a lot on your friends.

• Don't re-update from their answers if you think they don't understand the merits of averaging; you want to weight each person's raw impression evenly, not to overweight it based on how many others were randomly influenced by it (cf. information cascades: http://en.wikipedia.org/wiki/Information_cascade).
• Do re-update if your friends understand the merits of averaging, such that their apparent over-weighting of a few peoples' datapoints suggests they know something you don't (e.g., perhaps your friend Julie has won past championships in jelly-bean estimation, and everyone but you knows it).