Organizational Decision Making Research Paper

The main acquisitions in the field which will be exposed here distinguish models concerned with the ‘logic’ or with the ‘politics’ of decision making, and specify how they have contributed in understanding how actors can solve and do solve problems of knowledge acquisition and conflict resolution: how organizational decision processes follow different patterns according to the state of knowledge and to the configuration of preferences, and cope with uncertainty and diversity of interests.

2. The Logic Of Organizational Decision Making

The history of thought on organizational decision making did have a ‘big bang,’ with the ‘theory of bounded rationality’ (Simon 1955). The theory was developed in contrast with utility theory, the dominant model of rationality available at the time, developed especially in economics. The concept of bounded rationality included several components and submodels. First, it pointed out that actors (either individual or composite) on most problems cannot acquire the information required for utility maximizing calculations, either because it is too costly or because it is cognitively unfeasible to do so. Simon then distinguished ‘structured’ from ‘unstructured’ problems. In a structured problem, the actor knows what the relevant alternatives and the possible ‘states of the world’ are, is able to foresee the consequences of each combination of the two, knows what the value (or utility) of these consequences is for him. Even if a problem is structured, calculations of which action is optimal can be suboptimal or unfeasible because of computational complexity. As in the game of chess, the number of possibilities to be taken into account may be too high for the human (and computer) information processing capacity. In unstructured problems, the lack of knowledge—hence the state of uncertainty—is more radical. The actor does not know not only what the probabilities of payoffs are but even what the relevant alternatives, or the relevant consequences, or even the relevant objectives are. There can be uncertainty on cause–effect relations as well as on preferences (Thompson and Tuden 1959). In addition there can be uncertainty even on what has been observed, on what the ‘facts’ are. This state of knowledge has been called, in later research, ‘ambiguity’ (Cohen et al. 1972) or ‘epistemic uncertainty.’ Second, Simon’s research program invited ‘to develop a variety of models of decision processes based on bounded rationality,’ which could claim to be predictive of observed behaviors in varying conditions of information complexity. Simon outlined especially one of these possible models, the ‘satisficing model’ of search and choice. The simplest basic version of that model states that actors will accept the first encountered alternative superior to a given aspiration level, truncating search at that point. If it is difficult to find acceptable alternatives, aspiration levels fall, if it is easy they raise. In that version satisficing would describe primarily a decision behavior capable of generating ‘good’ solutions while reducing the costs of search. A more ambitious heuristic version of satisficing that would be able to solve some forms of epistemic complexity can be and has been defined: an actor formulates hypotheses about levels of result which are judged acceptable according to preference and reachable according to available knowledge; search for alternatives which are supposed to generate those results is started; either the objectives, the types of alternatives, the types of consequences considered, the cause–effect theories employed, or the observation and evaluation procedures employed, can be adjusted on the basis of experience (what proved to be possible to observe and find in reality) (Grandori 1984). Various other decision models, with claims of being able to manage even stronger conditions of uncertainty and conflict, have also been formulated in other strands of ODM research.

An ‘incremental model’ has been identified in research on ODM in public administration (Lindblom 1959). A starting provocative empirical finding has been that the best predictor of a year’s budget for a program or activity in local government is the budget of the previous year (Wildavsky 1964). In an incremental process the decision system should be able at least to discern where it wants to go: increase rather than reduce production capacity, improve levels of education, increase well-being. However, it can generate action without making use of theories on the cause–effect relationships that regulate how the action system works and changes. Rather it employs a ‘linear’ and automatic choice rule—try ‘incremental’ solutions that differ marginally from those in use and learn afterwards.

A ‘cybernetic model,’ built in the conceptual frame of control theory, also relies heavily on ex post learning, and more precisely on learning by reinforcement (Steinbrunner 1974). A cybernetic decision strategy implies only the following kinds of judgement: the capacity to recognize situations (e.g. a configuration of costs); the capacity to recognize performance gaps with respect to a standard (works does not work; positive negative); and possessing a repertory of possible actions that are applicable to eliminate the gap or respond to the situation, adjourned by a principle of reinforcement (repeat successful action, avoid previously failing actions). Processes that can be described and predicted fairly well as cybernetic processes range from some real-time unreflexive stimulus–response motivation processes to some innovation processes, especially the diffusion of observable and codified technological and organizational innovations.

A ‘garbage can model’ constitutes a limiting case of decision making behavior, in which actions are guided by the chance encounters between actors, with an opening of attention at a certain time, and the emergence of choice occasions (Cohen et al. 1972). It is supposed to be relevant especially under ambiguity: research using that model has typically been on institutions or processes with ‘ambiguous’ goals or highly unpredictable consequences (e.g., universities and cultural institutions, career systems). Although it has been seen as a provocative criticism of the meansends ‘determinism’ of rationalist models of ODM, the actual finding of the ‘garbage can model’ is that when ODM is not guided by clear knowledge and preferences, it is likely to be driven by a contextual ‘determinism’: choices are highly dependent on and constrained by the distribution of work loads and the time distribution of events (who is available for what choice occasion at what time).

Descriptive empirical research focused on various dimensions of ODM (see Butler 1997, Grandori 1995 for reviews)—e.g., ‘discontinuity,’ ‘cleavage,’ ‘complexity’—has generally supported the proposition that the higher the level of uncertainty and the conflict among interests becomes, the less comprehensive and deductively ordered the logic of ODM becomes; the less investment in ex ante knowledge acquisition and the more reliance on ex post learning is made; the less preferences are integrated and limited agreement is sought on action.

Prescriptive comparative assessments of decision strategies have also been elaborated in management sciences. Empirically, in structural contingency research, it had early been found that organization structures tend to set decision premises and routines so as to stimulate more optimizing and computational decision processes in units dealing with structured tasks and more heuristic and empirical approached in unstructured situations. Theoretically, also building on those results, prescriptive models for the selection of decision processes as ‘strategies’ have been formulated. In earlier applications of a contingency approach to decision behaviors (see Grandori 1984, for a review) the decision models available in ODM research have been ordered on continuums: from ‘closed system’ (planned maximizing) models to ‘open system’ (adaptive incremental) models; or from ‘analytic’ processes (based on calculation supported by formal support and explicit knowledge) to ‘un-analytic’ (noncalculative action driven by rules, conventions, and habits). Consistent with behavioral decision making research conducted at the individual level, the problem of strategy selection has been casted in those studies as a trade-off between effort and accuracy, finding that less calculative and comprehensive strategies become superior as task complexity and environmental uncertainty grows. Later, less deterministic ‘contingent decision processing’ models have been proposed, based on ‘decision failures’ analysis rather than on one-to-one correspondences between information states and decision strategies. In structured tasks any decision strategies are applicable, in principle, and strategy selection—as economists have often maintained—may be mostly a matter of information cost reduction and effort–accuracy tradeoffs. However, growing levels of uncertainty and of conflict among interests rule out progressively deductive and computational value maximizing strategies; theory-testing problem solving heuristic approaches; and finally even rule-driven noncalculative action approaches, leaving increasing terrain to the effects of chance and context—for reasons of lack of requisite knowledge and logical ‘impossibility’ rather than for information cost (which cannot be assessed anyway) considerations.

In the ODM research described so far, and also in recent authoritative overviews (March 1994), decision modes and strategies have been mostly seen as mutually exclusive alternatives. Recently, however, a more microanalytic view of decision processes as combinations and configurations of a variety of components and subrules has led to the possibility of mixed decision strategies; even more, to assert that some elements of each mode are necessary for any other (Lindenberg 1990): the possibility of conscious and critical decision making requires an embedding frame, accepted by convention, or learned by the experience of self or others; and building a model of a decision situation always requires some heuristic hypothesis testing, even in order to ‘optimize’ or in order to apply ‘appropriate’ solutions. Among recent trends in ODM, there has also been a tendency to deepen the implications of the ‘cognitive revolution.’ ODM research has sought to inquire more deeply in the processes of knowledge acquisition that frame decisions. If the main innovation of bounded rationality theory had probably been to analyze ODM with a theory of search, rather than just with a theory of choice, a subsequent and still actual challenge has been to connect the logic of ODM with the theory of knowledge. In organizational studies, this interest used to be cultivated especially at the border of the ODM field with organizational learning research and is now re-emerging at its border with the ‘knowledge-based theory’ of governance, with a main interest in the type of cognitive processes and organizational mechanisms which can sustain innovation. Research has also gone back to the founding connections between cognition and organization. In particular, there have been efforts at connecting research in cognitive psychology on heuristics and biases in decision making and ODM and organization systems and structures (Shapira 1997). These works have cast some doubt on the original intuition of bounded rationality theory in this respect—organization is a mean of reducing the limits of individual rationality—showing that organizational dynamics, systems, and structures can amplify rather than mitigate individual cognitive biases. A direction for future research, then, can be to discriminate between the conditions and the types of activities under which the two opposite effects are more likely.

3. The ‘Politics’ Of Organizational Decision Making

A different, although often connected, track of development of ODM studies have been concerned more with the problem of diverging interests and the relative ‘weight’ or power of actors holding them than on the problem of knowledge. Often the starting point has been a criticism of Simon’s ‘administrative man’ for ‘neglection’ of the political and strategic dimension (Pettigrew 1973). American ODM research on the politics of ODM has often contrasted organizational actors’ power with organization system’s efficiency, showing empirically that often the particularistic objectives and interests of subunits have more weight on the allocation of resources than the overall effectiveness and efficiency objectives held by the central agents of the system (Pfeffer and Salancik 1974). European studies have considered actors’ power in a somehow more positive light, casting the ODM problem as ‘the integration of the many,’ as contrasted with the ‘one actor theorem.’ Research conducted in that perspective has been especially concerned with the strategic use of uncertainty and the determinants of the decision weight of organizational units and groups (Crozier and Friedberg 1977, Hickson et al. 1971). It has been argued and found that the relative influence of organizational actors in ODM is a function of ‘the control of critical uncertainties,’ of the degree of substitutability, of the centrality in work and information flows, and of the perception and use of these ‘actors’ as bases of negotiation power.

As to the problem of how to govern pluralistic ODM system, negotiation has received most attention. Decisions have been seen predominantly as negotiated actions, taken within negotiated ‘rules of the game.’ Other coordination mechanisms are, however, clear contenders—and or complements—to negotiation in the governance of pluralistic and have also been studied in strands of organization studies connected or contiguous with ODM research systems (for a review of the relevant literature and authors, see Grandori 1995). ‘Organizational democracies’, based on the core mechanism of voting, constitute an alternative—if problems are structured enough; constitutional orders, regulated by the core mechanism of rules and norms, are another, at least in stable systems of recurring actions; ‘loosely coupled systems’, in which decisions are taken in a semiautonomous way and coordination is ensured by varying mixes of buffers, codified communication, or routinization of know-how may be thought of as another governance arrangement, suitable for complex but stable action. ‘Organized anarchies’—‘unsegmented structures’ in which everyone is free to address any problem and talk with any other actor, with little horizontal or vertical specialization constraint—would represent an extreme ‘governance’ form, in which there is no predefined pattern or mechanism of coordination, viable under conditions of extreme ambiguity and variability. These coordination systems have been seen, again, mostly as structural alternatives, each connected to some distinctive conditions of uncertainty and conflict of interests. As for decision strategies, however, coordination mechanisms could be assessed according to a ‘failure framework’ allowing for significant fungibility and equifinality among mechanisms, at least under simpler conditions. In fact, in a design perspective on distributed ODM, there has been an increasing awareness of the imperfections and paradoxes of any mode of governance of large systems composed by actors with differentiated knowledge and preferences. Although the variety of forms of ‘poliarchy’ mentioned above provide alternatives to the demonstrated failures of both market and hierarchical mechanisms in conditions of information complexity (Miller 1992), the limits of distributed decision making systems have also been examined. These systems are more costly— as to communication and integration costs, control losses and costs of delay—and their behavior is weakly predictable or more ‘indeterminate’ as far as there are usually many equilibria in such organizational games. Interestingly, in these contributions on ODM, mainly rooted in economics, a frequently recommended solution to the dilemmas of poliarchic governance is the institutionalization of framing norms and rules, which reduce the ‘degrees of freedom’ in actors’ preference variance and enhance the predictability of behaviors; i.e., the embedding or blending of rational choice (more or less behavioral) with the suspension of critical judgement and ad hoc calculation in certain zones of action.

Among other recent trends in the ODM field—as to the modes for ‘integrating the many’—there are signs of a growing concern for the equity dimension. The use of the concept of power has declined in studies of ODM, and the use of concepts of equity and organizational justice have gained terrain as the required complements to the notions of organizational effectiveness and efficiency in decision making. This may be seen as a healthy turn, as far as equity criteria are more precise and by definition fairer than power criteria. In addition, fairness criteria are logically necessary if the problem of governing pluralistic decision systems is tackled as in a prescriptive stance—how to devise superior arrangements for multiple actors—rather than just in a descriptive stance—how do multiple actors take decisions in organized settings.

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