The [minmax tree][1] method
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You basically have the AI simulate every possible move it can make and every possible response by the other players for several turns in advance, and then pick the path which is most beneficial to it.

The hardest thing about this algorithm is usually the rating function which determines what "most beneficial" means, but for an economy simulation you can usually express this in net-worth of the players (having more money and assets than the other players = good, having less = bad).

This method is very popular for games like Chess where you have a limited number of possible moves per turn, no randomness and no information hiding. According to your description your game seems to look like this. But you might be omitting some additional complexity.

The strategical rating method
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This seems to be the method Jon is describing in his answer. In this method you evaluate all possible decisions the AI can make and have certain rules which rate the feasibility of the move based only on the current game situation.

Such rules could be "do not sell when the price is lower than average" or "do not buy when the other players have a lot of the same resource". The AI values all these factors by assigning relative scores to them and then makes the decision which has the best score.

This method requires to have a deeper understanding of the game as a whole because you need to define rules and ratings of what is a beneficial or detrimental decision. It also makes the strategy a lot less flexible to balancing changes which affect which decisions are good or bad.

  [1]: http://en.wikipedia.org/wiki/Minimax