# Bayesian game and the set of types

In a finite Bayesian game, in most textbooks, a type of player $$i$$ is defined as $$\theta_i\in\Theta_i$$. Little is said about the "nature" of the set of types.

For example, could we have a two-element set $$\Theta_i=\{(a,b),(c,d)\}$$? If I am not mistaken this means that $$\Theta_i$$ is a subset of $$\mathbb{R^2}$$, right?

For the common prior then, we could have, for example, $$\mathbb{P}((a,b))=p$$ and $$\mathbb{P}((c,d))=1-p$$. Both probabilities describe the joint probability distribution that Nature assigns the types to the players.

Is this allowed in the definition of a Bayesian game?

I'd appreciate any help. Thank you.

Sure.

In the two type case there is an alternate solution that is frequently used. If $$a \neq c$$ and $$b \neq d$$, that is the types are really different in both attributes, then you can define a function $$f$$ for which $$b = f(a), \hskip 20pt d = f(c).$$ Whatever you need the second attribute for, this way it becomes a function of the first one, and the values of the first attribute completely define the type space.

Example:
In Spence's model of job market signaling, people have a type that shows how much they are worth to a company, how hard working they are. It is assumed that the cost associated with getting a degree is a function of this type as well, and harder workers get a degree more easily. One way to show this would be to include both worth to the employer and cost of getting a degree in the type attributes, but Spence only defined the types and gave some functions that mapped to these values.