## 1 Introduction  
Decision-making processes exist in many forms within computational models and simulations, and varied needs drive the scope of how decision-making is represented across modeling paradigms. Models are commonly developed under a specified context or experimental frame [1, 2], verified, validated, and tested under that perspective [3, 4], and decision-making representations' forms are dependent upon the utilized perspective. The desired levels of realism and aggregation for a model influences its form and impacts the insights that can be gleaned from a model when simulated. Depending on the modeled system, decisions can be responsible for dynamically altering the structure of the simulated environment, modifying behaviors or goals of simulated entities, ascertaining group membership selections, and determining the results of actions or interactions. Many factors can lead to differences in the desired type of decision-making representation utilized within a model, including: differences in the identified relevant model context [5, 6, 7]; differences in stakeholder perspectives [8, 9, 10]; differing perspectives on the importance of rare events versus likely outcomes [11]. [PERSON] and [PERSON] [12] describe decision analysis as a \"prescriptive approach...to think hard and systematically about some important real problems\" [12]. Thus, at its core, it helps us to understand how we should make decisions. It is the formal process of choosing from among a candidate set of decisions to determine which alternative is most valuable to the decision maker. Complicating most real decisions is that we wish to achieve multiple aims; that is, we evaluatecandidate solutions based on a potentially large number of criteria. For an apartment, we may consider cost, size, location, and amenities, whereas a job selection problem may cause us to consider salary, growth potential, location, and benefits. Each of these criteria is important to us and we need to consider them comprehensively to evaluate our problem using multi-criteria decision analysis (MCDA). MCDA is already being applied to assist in modeling and simulating systems involving decisions made by individuals or groups. MCDA has been widely applied for natural resource management as it provides a structured approach for integrating key management factors, captures the multi-functional uses of forests, and accounts for multiple stakeholder perspectives on how to best manage the forest [13]. With respect to public health safety, MCDA has been utilized to explore preventive programs for the prevention of Lyme disease [14] and to assess programs for preventing the spread of West Nile virus [15]. [PERSON], [PERSON] [16] compares the use of MCDA models against integrated assessment models in identifying alternatives for long term water supply planning at a town scale. Their study finds that all the models identified provided better performance than the current water supply system; however, the MCDA models also provided better value ranges and formed better bases for discussion than the integrated assessment models. We review and present a representative sample of commonplace techniques within MCDA. Our selection process considers techniques that are common for situations where there exist only a few attributes as well as situations where there may be many attributes. Situations pertaining to the representation of only a few attributes lend themselves to ratio assignment techniques, while scenarios with many attributes lend themselves to approximate techniques. The techniques reviewed and presented in our taxonomy rely on expert judgement, have been used in practice for over 30 years, and have been utilized in the personal experience of the authors in many research projects [17; 18; 19; 20]. We discuss how these MCDA techniques can be utilized for modeling decision making within three common modeling paradigms.