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Notes -
Right, but people maximize their individual utility, not the average of everyone else, so the equilibrium point may not be the globally optimum point. That's how public goods dilemmas work.
Ie, if we take the above instance, where the speed of each car is (0.99)^x, where x is the number of cars, then total throughput is x(0.99)^x, which is maximized at x = 100. If each person's utility when they drive is u = (personal speed) - 0.1 (and 0 if they don't drive), then the equilibrium (when an additional driver would have a utility of 0) happens at about 230 cars. And by definition at this point everyone gets a utility of 0, the wasted time and cost of driving is so bad that it just barely cancels whatever benefit would be gained from driving. Meanwhile the maximum for total utility among all drivers happens at x = 77, which is actually lower than the max throughput of 100 because those 77 drivers have better speed and thus gain more utility.
These are oversimplified dynamics and numbers, but hopefully they illustrate the concept. People frequently reach inefficient equilibria because they're optimizing selfish individual utility functions that don't consider externalities. And it's precisely those cases where the government can serve a legitimately useful purpose by nudging the equilibrium closer to the globally optimal value while still maintaining the feedback loops. Preferably by making people internalize their externalities in some way so that their personal incentives better line up with the global incentives so that they can still optimize but less selfishly. Hard quotas, limits, and bans tend to lead to worse results and/or have unintended consequences because they're not subject to appropriate feedback loops that reflect genuine preferences.
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