In this segment, we introduced a very central area of game theory called Mechanism Design. It's often called implementation theory and sometimes also called inverse game theory. And the intuition is that when we often think about game theory we think about a given game that was somehow, we inherited. A situation that we need to reason about and so we do. But in mechanism design, we get to design the game so as to optimize for certain qualities. Think for example about voting, we get to design the voting roles. It can be plurality or plurality of elimination of borda or [INAUDIBLE] elimination or any number of other mechanisms. Or think about selling a product. We can sell it directly by posting a price, or we can run an auction and there are many different kinds of auctions we could run. These are all examples of mechanisms and we'll be speaking about how to model this formally and what mechanisms aim to achieve. So when we design a mechanism, for example a voting rule, we don't design it in a vacuum. There are a some set of candidates for example, that we don't get to control. Candidates will be what they are. We certainly don't get to control who the voters are, they are who they are. And we don't get to look inside their head and decided what they should care about. Who they prefer to whom, that's a given, that's their type. Also part of their type is what their information, what do they know about various things. The setting we don't get to control any of that. So all that part, all that background that we don't get to control is called the setting or the Bayesian game setting. And what will happen, and we'll go over it now, but what will happen is we'll be given a Bayesian game setting which won't quite be a game. It will be a setting, a then we'll add to it the ingredients that will turn it into a game. And we'll try to design the game so as to optimize certain qualities. So let's go over it. We have this tuple, and let's go over it in order. So, N is simply the set of agents, and O is the set of outcomes. So, we have the social choice function that has certain outcomes. For example we have a voting situation with certain possible candidates, and that's at O. Then we have those types, those are exactly the objects we saw in Bayesian games. Again we don't yet have a game here but the notion of type is well defined. And so we have, and type is all that private information to agents. So intuitively, think about it as, in a voting situation, what is my preference ordering of the candidates? What do I know about the preference ordering of other agents? What do I know what about they know about my preference ordering? And all of that, all that private information is my type. We have, as we usually do, a common prior probability distribution over those types that's commonly known by the agent. And we have a utility function that says, given an outcome and given the types of the agents, what is the utility to each of the players? And so, given that my preference ordering is what it is. And given that a particular candidate was elected for example, would give me some utility. This is all the setting that we, as mechanism designers, do not get to control, and don't have access to. We know perhaps who the agents are, we don't know their types. We may know their common prior on those types. So now, what is a mechanism? A mechanism consists of those things that when added to the Bayesian game setting would make for a Bayesian game. So there's two things that missing are the actions. So, so far we had no action for the agents. So mechanism simply specifies the action, for example for voting rules, or the auction rules. And it also specifies because the actions, if they just live in vacuum, don't have any force. So they need to interact with the settings somehow, the action simply specify the outcome or more generally, a distribution of the outcome. So, action, simply a set of actions for each of the agents. For example, in a voting situation, it might be to specify your entire ordering among the candidates. Or to specify your top candidate, whatever the voting rules are. And in mapping says based on those actions, for example, votes, who is the winner? And as you see for technical reasons we don't necessarily find one winner or one social choice outcome but a distribution over those outcomes. Often for your intuition think of this specifying a specific outcome, you usually won't lose any intuition you've think about it that way. And so this is a mechanism given a Bayesian game setting. You add these two ingredients, the actions and how they map to outcomes. And when you put those together, you get a Bayesian game. So how do you specify the mechanism? Well, the intuition is that we want the agent to behave a certain way. We would like to reach a certain social choice outcome that's best. For example, we might want to find a socially optimal Pareto efficient outcome. We might want an option setting 2s, option setting to maximize our revenue, and do that while not having control over the Bayesian game setting. And so the trick is to set up the rules of the game, the mechanism, to cause agent to behave the way we want them. Even though we don't have access to the internal information, and can't directly control the actions. But we'll set the rules of the game such that by their acting in their own perceived self interest, they will lead to the outcome we desire. And so it's really, you can view it in a number of ways. You can think of it as an optimization problem with only partial information about and control over the variables you're optimizing. You can think of the setting as a set of Bayesian game and you're picking, selecting a particular Bayesian game out of the set. And really perhaps the most basic view of it goes back to one of the terms, implementation theory. And we really want to implement a social choice functiob. Which if we had access to the agent's type we could have done directly, but we don't. If we simply asked the agents, it may and may be in their interest to deceive us. And so our goal is to implement it nonetheless. And there's something magical about this setting. And it's quite a robust area within game theory. So what does it mean to implement a social choice function? It in fact has several possible meanings. One of them, among the strongest, is implementation in dominant strategies. What does that mean? Well, let's assume we start with a Bayesian game setting. And we add to it a mechanism and we'll say that that mechanism implements a social choice function, C. In dominant strategy, if it's the case that, pick any utility function for the agent,, any vector U of utility functions, one for each of the agents. If the game has an equilibrium in dominant strategies, such that in any such equilibrium, because there could be multiple dominant strategy. But any such equilibrium we have that the outcomes specified by the mechanism is indeed a social choice function. Would in that case we would say that the mechanism implemented the social choice function in dominant strategies. You could ask several questions about it. Why should it be in all equilibrium? Maybe it's enough that in one, indeed you could define it that way. Why only dominant strategies? And in fact, the answer is, it doesn't have to be in dominant strategies. So a more general, more relaxed definition of implementation is implementation In a Bayes-Nash equilibrium. And it is almost the same. We say that given this Bayesian game setting, let say that the mechanism implements the social choice function. In a Bayes-Nash equilibrium as opposed to a dominant stretch equilibrium. If there exists a Bayes-Nash equilibrium of the game that results from the mechanism intercepting. Such that for every type of agent and every action profile that can arise in this equilibrium. It's the case that the outcome defined by that action profile is a social choice function of the agents given their types. So it's an absolute bit of a mouthful here. But the intuition is very much the same as in the dominant strategy implementation. In dominant strategies, the situation is simple. We simply gave dominant strategies and we require that in all of those the action profile lead to a social choice function. Here we have probability distributions and so there could be multiple actions that instantiate these action profile, these strategies. And what would be the case that every action profile would lead up, by definition, to some outcome according to this mapping. And we would like to be the case that indeed there's a social choice of the agents, and they are, meaning they have their own preferences. And with respect to those preferences, we want that action profile to lead to the optimal outcome as defined by the social choice function. So as I said earlier, mechanism design is a very rich and deep topic of game theory. There's a lot to say about it, both from the point of view and it's also among the parts of game theory that have lent themselves most to applications. Most noticeably to auctions, and there are separate lectures about that. But let me for now stay at a more abstract level, and make a few comments about Bayes-Nash implementation. First of all, there could be multiple equilibria. And we back to the same issue that we have in game theory in general even before we speak about mechanism design. When we have multiple equilibria of a game, what do we actually predict that will happen? Now, in a mechanism design setting, we could say if I have multiple equilibria, is it enough that I select one of them? And require that that equilibrium always lead to social choice optimum or not. We have the usual concerns about the equilibrium in general. Equilibrium is a very strong notion. What happens if agents, first of all, somehow miscoordinate on the equilibria, play different as that profiles corresponding to different equilibria. Or even more extremely don't play an equilibrium at all. A legitimate question in game theory general and in particular here. Also equilibria are very general category and one objection could be that asymmetric equilibria are implausible here. There's no reason to think that when present in a game, one agent would gravitate to one strategy and another agent to another, even though the setting is completely symmetric. And so there are various things you can do about these questions and concerns. One of them is simply require that the implementation be asymmetric Bayes-Nash Equilibrium. That takes care of the asymmetry concerns. You might want also to require ex-post implementation, meaning that the strategies selected are an ex-post equilibrium, a very strong notion. So an agent don't experience regret for having selected those equilibria even after the game is played. Going beyond issues specific to Bayes-Nash implementation on implementation of social choice functions in general. We can require indeed that it arise in some equilibrium or in every equilibrium. We can even require that there be only one equilibrium in which it requires. And finally there's various ways to have to implement a function. And one important distinction to keep in mind is between direct implementation and indirect implementation. In a direct implementation, agents send essentially a single message to the mechanism designer. The center disclosing whatever they need to disclose about their type. And then, the risk happens in the mechanism, whereas in indirect mechanisms, there's an inter-process of messaging back and forth. Think even about voting as an example. You could simply disclose once and for all something, for example, your entire preference profile. I prefer A to C to B, or if the mechanism calls for only disclosing a top choice, say I simply prefer A to the others, and that's it. That'll be a direct mechanism, whereas an indirect mechanism I would say, for example, in a voting situation. Might say so let's speak about elimination, you'd declare a top choice and then again, you declare the top choice among the remaining candidates. You might, so there'd be an unfolding process. Direct implementations will turn out to be quite universal in what they can accomplish. And much easier to analyze than indirect implementation and so for theoretical investigation purposes at least, they are quite central.