S Schuster et al.
Technion – Israel Institute of Technology
Personal Comment: My apologies, but I had serious weirdness on my machine which necessitated a restart of my window manager. Therefore I missed the first half of the talk.
Prisoner's dilemma: if prisoner A reveals the plan of escape to the jailor, while B does not, A is set free and gets £1000. B is kept in prison for 10 years. Various other rules produce different results. For more information, see http://en.wikipedia.org/wiki/Prisoner%27s_dilemma. The payoff matrix shows that if both cooperate, both are set free. If A cooperates and B doesn't, he is punished for his goodwill. If both defect, then both end up with prison: this ending the Nash equilibrium. For microorganisms this can be relevant because there is just the issue of mutation, without blurring the issue with trust etc. 🙂
The snowdrift game is two drivers are stuck. Only the first is needed to shovel to get out, so the other can help, but it doesn't gain them anything. Info on comparison of snowdrift and Prisoner's dilemma available (just a quick google search, haven't properly read it) at http://www.physorg.com/news111145481.html.
John Maynard Smith extended these games for populations. In population dynamics, the only evolutionary stable situation (ESS) for the prisoner's dilemma is that the whole population defects. Apply this to the situation of the glucose gradient around the yeast cell – cooperators get an advantage. The relative fitness of the defectors are dependent on cell number (density).
Under physiological conditions, often snowdrift game, supported by recent experimental data (Gore et al 2009). See also J.Biol. Phys. 34 (20087) 1-17. This is good news for biotechnology, in that it is possible for exoenzyme-producing strains will not necessarily be outcompeted by non-secreting mutants.
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!