There are many conceptualizations and formalizations of decision making. In this paper we compare classical decision theory with qualitative decision theory, knowledge-based systems and belief-desire-intention models developed in artificial intelligence and agent theory. They all contain representations of information and motivation. Examples of informational attitudes are probability distributions, qualitative abstractions of probabilities, knowledge, and beliefs. Examples of motivational attitudes are utility functions, qualitative abstractions of utilities, goals, and desires. Each of them encodes a set of alternatives to be chosen from. This ranges from a small predetermined set, a set of decision variables, through logical formulas, to branches of a tree representing events through time. Moreover, they have a way of formulating how a decision is made. Classical and qualitative decision theory focus on the optimal decisions represented by a decision rule. Knowledge-based systems and belief-desire-intention models focus on an alternative conceptualization to formalize decision making, inspired by cognitive notions like belief, desire, goal and intention. Relations among these concepts express an agent type, which constrains the deliberation process. We also consider the relation between decision processes and intentions, and the relation between game theory and norms and commitments. © 2003 Elsevier B.V. All rights reserved.