Integrative Complexity: an Approach to Individuals and Groups As Information- Processing Systems Author(S): Michael J

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Integrative Complexity: An Approach to Individuals and Groups as Information- Processing Systems Author(s): Michael J. Driver and Siegfried Streufert Source: Administrative Science Quarterly , Jun., 1969, Vol. 14, No. 2, Laboratory Studies of Experimental Organizations (Jun., 1969), pp. 272-285 Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management, Cornell University Stable URL: https://www.jstor.org/stable/2391105

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This content downloaded from 67.242.27.73 on Thu, 17 Dec 2020 18:25:09 UTC All use subject to https://about.jstor.org/terms Michael J. Driver and Sieyfried Streufert

Integrative Complexity: An Approach to Individuals and Groups as Information-processing Systems

Individuals and groups can be viewed as information-processing systems which respond in a curvilinear fashion to three components of input load: complexity of information, noxity (unpleasantness) and eucity (pleasantness). An optimal input load is postulated, at which each system is expected to achieve maximum complexity in information-processing. At similar input levels, some systems are expected to show more complex information-processing than other systems. Research is reviewed which suggests that the model holds for perception, information search, decision-making, and innovation. When productivity criteria are associated with complex information-processing, the model predicts productivity. A more complex phasic theory is then advanced, which argues that perceptual and decision-malking functions are separate and not synchronous.

One of the primary objectives of management bit means the reduction of uncertainty by one science is to understand and control the group half. Although this technical view has yielded processes in highly productive organizations. In some exciting results (Garner, 1962), it seems this discussion productivity is viewed as strongly too restrictive and has led some (Schroder, dependent on the information-processing char- Driver, and Streufert, 1967) to redefine infor- acteristics of individuals, groups, and organiza- mation as anything that alters subjective (or tions. Information can be regarded as the objective) probabilities or utilities. Thus in thread out of which all production decisions this discussion, any environmental change which are woven. The particular way in which organi- could cause a shift in expectations or evalua- zations search for information and handle it is tions will be termed information, regardless of therefore of great importance in developing a whether certainty is decreased, increased or is model for productivity. unaffected, so long as utility (i.e., value times This discussion outlines a new model of probability) is altered. information-processing in systems of all types- A second and crucial concept is system. The individuals, groups, and organizations.1 Some use of the term is based on von Bertalanffy recent experiments testing this model are pre- (1952) and general systems theory (Miller, sented and discussed, and a further extension 1965). Von Bertalanffy, in his important of the model is then proposed. treatise on biology, defined a system as a "complex of elements in mutual interaction." THE INFORMATION-PROCESSING He proposed that all sets of organized elements APPROACH TO PRODUCTIVITY follow certain general rules. These rules he Definitions termed general systems theory. In this discussion, individuals, groups, and organizations First, it is useful to define key concepts. Of are regarded as systems which possess certain central concern is information. In Shannon & general characteristics in common. It is recog- Weaver's (1949) information theory, this term nized that there are serious dangers in making has come to mean something which reduces unwarranted analogies between systems of uncertainty or entropy in a receiver. Informa- different levels of analysis, for biological, per- tion has been quantified as "bits," where each sonality, and social systems differ in many significant ways. Nevertheless, for present 1 This paper is based on an invited address presented at the Institute of Management Science meeting, Feb- purposes, individual or personality and social ruary 1966, Dallas, Texas. systems are treated as having certain basic

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similarities in the organization and structure of input to the complexity or organization of their parts, and are therefore expected to re- biological and social systems. He argued that spond in basically similar fashion to certain the more complex the input, the more complex aspects of the environment. the system. In information-processing terms, A third basic concept is input. Most gener- this means that the complexity of the input ally, input is any condition initially external directly determines the organizational capacity to a system which upon contact with a system of a system to process information and produce produces observable change in the organization complex outputs. or behavior of a system. Individuals, groups, Some of the earlier research of Miller (1960) and organizations are considered systems which on information-processing in individuals and operate to process inputs into outputs. The groups seems to offer partial support for Spen- inputs which are of concern here are not defined cer's view. Miller found that as he increased as energy units as in physical systems, but the complexity (i.e., number of units per time rather are defined as information units. An period and amount of information per unit) of information unit is any discrete condition of information input, both individuals and groups the non-system environment capable of altering increased information-handling capacity linearly the utility or probability patterns in an indi- up to an upper limit or asymptotic level. At the vidual or group. The nature of this information asymptotic level, however, the systems began unit in any setting is arbitrary. One could to omit inputs or let them accumulate in a define units as messages, sentences, words, or delayed action "list"-a process termed "queu- the presence or absence of a colored light de- ing." It seemed, therefore, that each system pending on the refinement of measures of had an upper limit for input beyond which change in the utilities or probabilities of the Spencer's linear formulation was not valid. Some information-processing systems involved. In- interesting work of Chapman, Kennedy, Newell, dividuals, groups and organizations are con- and Biel (1959) on a simulated air-defense ceived as information-processing systems, which system showed a similar, general rise in informa- respond in characteristic ways to variations in tion-processing capacity to an asymptotic level. information inputs as they produce outputs. More recently, Miller (1962) found that if The last concept is concerned with a crucial he continued to raise the complexity of the aspect of input, the sheer complexity of the input beyond the previously obtained asymp- information. This is of central importance in totic level, the utilization of information began understanding systematic responses to varia- to decline in both quality and quantity. Chap- tions in input. Complexity of input can be man et al. (1959) found a similar result in defined as the number of utility or probability perception of input, if not in final output. This changes that an input can potentially evoke in led Miller to consider some data which ranges a maximally open system as a result of the from physiological systems to social organiza- informational content of the input. A "maxi- tion systems. For instance, Granit and Philips mally open" system is one in which there are (1956) found that in the Purkinje cell of the no mechanisms blocking the perception of any retina, the response rate first increases with implications in the input. As will be seen, many input, then decreases. Jung and Baumgartner information-processing systems "close out" in- (1955) found a similar curvilinear relation be- formation and do not cope with the full com- tween input complexity and the output of the plexity of the input. optical cortex in the brain. At the individual person level, Quastler and Wulff (1955) found Linear and Curvilinear Relationships that if input complexity (i.e. number of notes Between Input and Output per bar) was steadily increased, the quality of There have been a number of attempts to musical output in accomplished pianists first develop models relating the complexity of the improved, but then deteriorated. Anderson and input to the functioning of human information- Fitts (1958) reported that three input dimen- processing systems. One of the simplest and sions were better than two or four in inducing earliest formulations was proposed by Herbert maximum information transmission by human Spencer. Spencer (1897) suggested a simple beings. At the group level, Lanzetta and Roby linear function for relating the complexity of the (1956) found an analogous curvilinear pattern

This content downloaded from 67.242.27.73 on Thu, 17 Dec 2020 18:25:09 UTC All use subject to https://about.jstor.org/terms 2 74 ADMINISTRATIVE SCIENCE QUARTERLY with an optimal input level for group perform- integration, to input complexity can hardly re- ance. Finally, Fritz and Grier (1954) identified sult in a curvilinear relationship. By contrast, a similar curvilinear function between input the studies to be described employ output mea- complexity and output quality in systems of sures that amply reflect internal changes in the air-traffic control. integrative complexity of the system caused by Many studies, however, do not show the change in input complexity. curvilinear relationship between input complex- The integrative complexity model hypothe- ity and system output. There are many possible sizes that individuals and groups can be re- reasons for this deviation, but two factors have garded as information-processing systems; that been included in a new information-processing the internal integrative complexity of a system model proposed by Schroder et al. (1967): (1) varies in a curvilinear pattern as the complexity the nature of the system output and (2) the of the input changes; and that therefore sys- differences between systems in information- tems have optimal-input conditions (neither too processing characteristics. complex nor too simple), which maximize their capacity for integrated complexity in output. Relation of Integrative Complexity They further postulate that complexity of input to System Output has three components: (1) the amount of Schroder et al., suggested that not all output probability change implied (e.g., rate of infor- is systematically related to the complexity of mation input), referred to as pure information the input. They proposed that it is only the or load; (2) the amount of attainment of posi- integrative complexity in output that is related tive values (e.g., reward, referred to as eucity; to input complexity; their view is that informa- and (3) the amount of attainment of negative tion-processing systems can be described in values (e.g., threats), referred to as noxity. terms of (1) the number of parts at work in a They argue that each of these three components system (i.e., roles in a group, ideas in a per- contributing to the complexity of the input sonality, etc.), termed differentiation; and (2) bears a curvilinear relationship to the integra- the amount of interconnection between parts, tive complexity of a system and that they termed integrative complexity. The more a probably jointly affect information processing system is differentiated and integrated, that is, in a roughly additive fashion. A crucial feature the more system parts are interrelated, the more of this view is that the components, pure infor- the potential information in the input is utilized mation load, eucity and noxity have inter- in outputs. Output integrative complexity (e.g., changeable effects on information-processing the number of tactics relating to a goal) thus systems; e.g., threat and rate of information mirrors the integrative complexity of the parts in the input could have similar effects on the in a system. As input complexity affects the integrative complexity of systems. Walker system's integrative complexity in a curvilinear (1965) arrived at a very similar view of the way it therefore relates to the integrative com- relations among these variables after analyzing plexity of the system's output in a curvilinear a body of data collected by himself and others way. A high degree of integrative complexity on behavior of sub-human mammals. in a system implies that outputs such as de- The basic integrative-complexity model is cisions, verbalized perceptions, or performance summarized in Figure 1. The environmental will reflect extensive prior use of information input (input complexity) is a roughly additive and that therefore these outputs will be char- function of information load, eucity, and noxity acterized by a high degree of relatedness among factors. At present, the evidence (e.g., Schroder parts. Integrative complexity of output is often et al., 1967) suggests that an additive relation- what is meant by the quality of the output. ship is found for at least part of the ranges of Schroder et al. maintained that only when the these variables, but further work will be needed quality of the output-or the degree of integra- to determine more precisely the interaction of tive complexity in the output-is assessed, can these variables over all input ranges. one obtain a clear curvilinear relationship with It will be seen in Figure 1 that the complexity input complexity. Thus, for example, studies of input into a system has an effect on system relating some simple response (e.g., conditioned complexity in addition to its varied impact on eye blink), which requires little information system content (e.g., concept learning). If the

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FIGURE 1. FLOW DIAGRAM OF INTEGRATIVE COMPLEXITY MODEL.

input complexity is moving away from the optimal level; and that current non-optimal optimal range of the system, the complexity of input induces only temporary deviations in the the system temporarily decreases. If the input complexity of the system away from the unique is moving toward the optimal level, the com- maximum associated with the optimal input plexity of the system temporarily increases. If level of the system. However, it is suggested the input is within the optimal range, the sys- that sustained deviations from the optimal input tem maintains operation at its maximum complexity may cause permanent changes in capacity for complexity. Note that both the the entire response range of a system, especially differentiation of parts and the integrative in regard to the maximum complexity which complexity of the structure of the system are the system can attain (see Schroder et al., involved in system complexity. 1967). Only two final points need be made in ref- Differences in System Characteristics erence to Figure 1. First, not all observable behavior is a direct function of the complexity The idea of different optimal input levels for of the system. Much of the behavior of an individual or group systems leads to the second individual or group system is most clearly related reason why a curvilinear relationship is not to the content of the system, i.e., to past per- always observed between system input and out- sonal habits or group norms. However, when put. Schroder et al. (1967) argue that systems observable behavior strongly reflects integration have different optimum input ranges which re- of new information, e.g., in a decision for which flect different capacities for maximum complex- there are no precedents, then the complexity ity. Furthermore, as input moves away from of a system is a significant factor in predicting the optimum, each system responds with a behavior. characteristic temporary loss in integrative Furthermore, the optimal input complexity complexity. Each system then has its own for any system is not the same. Schroder et al. curvilinear pattern of change in integrative (1967) suggests that the past experience of an complexity, which is consistent and reliable individual or group system determines its unique over time. The curvilinear pattern of reaction

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to complexity in input is called the character- 2 are based on actual research findings (Streu- istic level of integrative complexity in the fert and Schroder, 1965; Streufert and Driver, system. This idea is quite distinct from older 1965; Driver, 1962; 1969). "trait" theories of social science in that an The data suggests that all information- individual, group, or organization is not ex- processing systems show roughly symmetrical pected to display the same amount of system curvilinear reactions to input, and that more complexity in overt behavior in all situations; complex systems (e.g., A in Figure 2) are al- rather, each system should react with a predict- ways more complex at comparable inputs than able curvilinear pattern of system complexity simple systems (e.g., B), except at extreme as the complexity of input changes. In general, inputs. The data also suggests that at very low a system with a high characteristic level of or high inputs, systems operate at extremely complexity shall be referred as "complex" and simple, often one-dimensional levels (Driver, a system with a low characteristic level of 1962; 1965; 1969). The curves of Figure 2 complexity shall be referred to as "simple." are found in the research literature if one varies Information-processing systems can thus be either load or noxity-eucity, and holds the other classified as displaying a characteristic cur- variable constant. Their additive effect is only vilinear pattern of integrative complexity across an operating assumption, which may yield to a the entire spectrum of complexity in input con- more complex interaction formulation with new ditions. There is therefore not one curvilinear studies. pattern relating input and output, but rather a Figure 2 shows a possible source of confusion whole family of such curves. Figure 2 repre- in information-processing literature. If systems sents two of these curves, showing two systems A and B are compared at different input conditions (e.g., A at point 1 and B at point 2 in Figure 2) B may appear to be more complex HIGH in output than A. Some of the confusion in the literature may stem from the mixing of systems

Z OPTIMAL INPUT 0 with very different curve patterns or from a lack of comparability of input conditions. (LOW\ ( HIGH CHARACTERISTIC It must be emphasized that the integrative Z / \ ~~~~~~~~~~~~~~LEVEL LL]SYSTEM OF INTEGRATIVE A < COMPCIY TY) complexity model does not equate individuals and groups on all aspects. It only states that both types of system can be characterized as showing more or less integrative complexity, TIVE . (LOW CHARACTERISTIC of different characteristicLEVEL OF INTEGRATE ite both will respond in a curvilinear fashion to input, and both have characteristic levels of integrative complexity that represent their unique curvilinear response to input. Further-

cc~~~EVROMNA NU COMPLEXITY) more, individual and group levels of systems interact. Integrative complexity in individuals tends to produce integrative complexity in groups (Tuckman, 1964; Stager, 1967), but the relationship between group complexity and individuals is not simple. It is probable that if group structure is slightly more complex than the personality structures of the individuals comprising the group, some increase in the complexity of the personality structures may occur. Also if the group structure is simpler or far complexity. Systemr A is a more complex more complex system than its members' individual than System B. Systems A and B could be structures the complexity in individual per- individual or group systems; the same dynamics sonality structure may decrease. Far more re- should hold in either case. The curves in Figure search is needed before the relationship between

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the characteristics of group and individual RELATION OF PRODUCTIVITY systems can be fully resolved. TO COMPLEXITY OF OUTPUT

To link productivity to the integrative com- Motivation plexity model, those system outputs which Besides the capacity to handle input, infor- reflect variable integrative complexity in sys- mation-processing systems are also characterized tems and contribute to various conceptions of by their motivation to seek and use information. productivity must be considered. We will Recent trends in motivational analysis (White, examine such outputs as: the amount of in- 1958; Hunt, 1963) suggest that classic motiva- formation used in perceptions, the amount of tional constructs such as drives (e.g., hunger) complexity in decisions, the amount of com- and affects (e.g., fear) no longer can be re- munication, the extent of information search, garded as primary sources of all behavior. For and the degree of innovation in behavior. instance, one animal study (Montgomery, 1952) found that hunger arousal actually diminished Information Processing and exploratory, information-seeking behavior, Group Productivity while another study (Montgomery and Monk- Perception. Two studies were particularly man, 1955) revealed that fear had similar concerned with perception (Streufert and inhibitory effects. Clearly the arousal of strong Driver, 1965; Driver, 1962; 1969). One deals drives or emotions is not motivating this infor- with the effects of pure information load on mation oriented behavior as classic motivation perceptual complexity, while the other con- theory would suggest. Yet, animals and human siders the effects of noxity-eucity variables on beings clearly work and learn merely to obtain perception. The basic finding was that the information. In one classic study, Butler (1957) pattern illustrated in Figure 2 was substantiated found that monkeys would learn and persist whether load or noxity-eucity was varied. In at the most difficult problems, merely to be both studies perceptual complexity varied in a afforded the dubious pleasure of looking at the curvilinear fashion with increasing input load. experimental laboratory through a peephole! In Streufert and Driver's study, information This kind of data has led Hunt (1963) and load was varied by controlling the number of others to postulate biological "cognitive moti- messages per half hour in a tactical game de- vations" inherent in information-processing veloped by Streufert et al. (1965a). The game itself. Cognitive motivation is not derived from confronts two teams in a military negotiation other motivations such as drives. The motiva- situation analogous to Viet Nam. Perceptions tion to seek a certain optimal amount of com- of the other team were filled out by each team plexity in one's environment is suggested as one and rated for complexity using methods de- such cognitive motivation. This motivation has scribed by Schroder et al. (1967). Some factors a sound basis in animal data (Denenberg, 1964; considered were: empathy, cause-effect pat- Walker, 1965) and can be suggested as a con- terns, three (or more)-step inferences, and comitant of various levels of integrative com- imputation of intentions to others. While plexity as defined in the integrative-complexity noxity-eucity factors (e.g., success-failure) were model. It can be argued that each information- held constant, perceptual complexity varied in processing system has an inherent motivation a curvilinear, symmetrical way with input load. to maintain input conditions at an optimal level, The complexity (i.e., characteristic level of which maximizes its internal integrative com- complexity) of the subjects in the tactical game plexity. Thus, the model suggests that systems was assessed using projective measures described differ not only in information-processing capaci- by Schroder et al. (1967). For example, in the ties but also in motivation for a particular amount of input complexity. Although there method of Impression Formation, subjects wrote are supporting data for individual human and a one page description of a "person" who was infrahuman systems (Bryson and Driver, 1969), described to them only by 3 pairs of potentially there are few data on the preference for com- conflicting adjectives (e.g., intelligent-stubborn, plexity by groups. Here again one must use industrious-envious, etc.). The descriptions caution in making analogies from individuals could be rated for general level of integrative to groups. complexity using a manual developed by Schro-

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der et al. (1967) and by Streufert and Driver found between the average perceptual com- (1967). plexity and general interest-threat level. Maxi- In the tactical game, half of the subjects mum complexity was found in runs with high were complex (i.e., high characteristic levels interest and relatively low threat. Threat was, of integrative complexity) and half were simple in general, negatively related to perceptual com- as measured by scores on tests such as the Im- plexity. Subjects were divided into groups of pression Formation Task. Intelligence test high and low characteristic levels of integrative scores showed no significant differences between complexity using methods of selection similar these two groups in I.Q. Both groups showed to those described for the tactical game. Again curvilinear reactions to! load; however, at all it was found using multidimensional scaling comparable load conditions, the high-complexity that the perceptions of the more integratively group displayed more integration in perception complex subjects were more complex at all than the low-complexity group. The empirical levels of input noxity-eucity except the most curves resembled those of Figure 2. Both groups extreme. peaked at the same optimal input level. The These two studies illustrate the basic integra- differences between groups were greatest at the tive complexity model quite well. Not only do optimal input and decreased as lower or higher they show how perception-the gateway to all loads were approached. More recent work in information processing-is affected by input progress suggests that the simultaneous peaks complexity and the integrative complexity of of high and low-complexity systems in data group members, but they also reveal the ap- from the tactical game are due to unrefined parent interchangeability of the components measurement. More refined measurement cur- contributing to input complexity (e.g., threat- rently suggests that simpler systems may peak interest and input load). at less complex input conditions than complex Information search. Besides perception, in- systems. formation search behavior can also be related The second perception study (Driver, 1962; to the model. Information search may be 1965; 1969) found basically similar results simple, defined as a request by the subject to when noxity-eucity was varied, with input load some external agency for more information; or kept relatively constant. In this study seven complex, defined as the use by the subject of teams of players simultaneously operated some strategy upon the environment to generate simulated "nations" in Guetzkow's Internation information. Simulation (Guetzkow et al., 1963). A run The research showed a high level of both consisted of seven teams operating for 5 days; kinds of search with low input loads. Increased there were 17 separate runs of the simulation. input loads related to decreased search. The In two runs, the communications data indicated relationship between search and noxity-eucity low eucity (interest). The remaining 15 runs is not yet known. The linear relation in search were scaled for threat (noxity) level using an behavior demonstrates that it is only partially analysis of armament and treaty data. Thus controlled by the complexity of a system. the runs formed a continuum from low interest Particularly at low input levels, content factors, (eucity), to high interest and low threat, to high such as social desirability, operate to sustain interest and high threat. high search levels. It is only at higher loads For each run the average perceptions of the that the resultant simplification of individual 7 simulated nations by the players were assessed or group information processing structures over- using multidimensional scaling (Jackson and rides content factors and forces the abandon- Messick, 1963), a mathematical technique for ment of search as one means of reducing the uncovering the basic perceptual dimensions overload on a system. integrated in judgments of general similarity A difference in search behavior between among stimuli. The average perceptions so complex and simple individual systems is also scaled, showed both the differentiation (number found. In general both types of search are em- of dimensions) and integration (weighting pat- ployed by high-complexity and low-complexity tern of dimensions) of perception in each run. systems at low input loads, but the complex Using either differentiation or integration as a system tends to use complex search tactics, measure of complexity, a curvilinear pattern was while the simpler system tends to use the simple

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search tactic. This bias becomes very marked levels display preferred modes of information under high load conditions, when the nonfavored search. tactic drops out of use much more readily than Communication and innovative interaction. the favored tactic. Complex, multilateral communication efforts in The most complete data on search behavior a social situation may be regarded as a form of are found in a study (Streufert et al., 1965b) complex information search. In the Internation using the tactical game described by Streufert Simulation study, cited above, it was found that et al. (1965a). Two search tactics were per- communication varied as a function of the char- mitted: simple search-requests for informa- acteristic level of integrative complexity of tion from "intelligence" sources, and complex decision makers in the groups (Schroder et al., search-orders by players to initiate reconnais- 1967). Communication was assessed by count- sance-type missions by their own units that ing the number of press releases given to the might provoke useful responses from the newspaper published in each simulation run. "enemy." Input load was varied by manipulat- There was a significant tendency for the more ing message rate. Half the players were of complex systems to send out more press releases, complex characteristic levels, half were simple. regardless of differences in input conditions. Measurement of individual complexity was The suggestion is that communication may be a handled as described above. still more complex search tactic than the self- The results revealed a general decrease in initiated tactics of the tactical negotiation game, search from very high to moderately high levels and therefore emerges even more clearly as a as input load increased. At no time was search tactic of complex systems. abandoned. Complex players maintained rela- Another tactic analyzed in the Internation tively high levels of complex search, particularly Simulation is the initiation of trade relations at high input load; they rapidly abandoned with simulated nations outside the nation alli- simpler search at high input loads, although ance "blocs," which were defined at the game's they made high use of simple search at moderate beginning by the experimenter. It was found loads. Simpler players favored simple search, that, except for groups which had high levels particularly at higher input loads. Each type of military-economic power, the groups com- of system seems to maintain a search tactic posed of more complex subjects engaged in suited to its complexity level as long as possible, significantly more trade outside defined blocs while using the other tactic as a supplement. than groups with less complex members. This Note, however, that simple systems used the result suggests that another complex communi- supplementary complex strategy most at lowest cation and information-gaining tactic-setting input loads, while complex systems used their up general trade relations-is a more typical supplementary strategy most at moderate input tactic of complex systems. This result also -possibly indicating that maximum search is suggests that more complex systems are more attained by complex and simple systems at dif- likely to engage in innovative, creative tactics ferent input loads. in dealing with input. So long as one considers It appears that preferred complexity of search tactics dictated or sanctioned by an external tactics varies directly with the complexity of source or authority, one finds either no dif- systems. In a study of simulated stock market ference between complex and simple systems behavior by Driver (1960), teams composed of in behavior (as for example in trade within the subjects of high and low complexity operated pre-established blocks) or a tendency for brokerage firms in a simulated market. Several simpler systems to produce more of the output sources of information were available, includ- sanctioned by authority (e.g., delegated search). ing an investment analyst's report that provided However, when tactics require original, complex, a simple, quasi-authoritarian method of obtain- self-initiated, innovative behavior which may ing output instead of more complex self-initiated ignore or violate authority, complex systems methods. Subjects were asked to rate their seem more adapted to the activity and generate preference for information sources. The less more of this kind of output. For example, Tuck- complex subjects significantly preferred the man (1966) recently found a strong positive simpler input source (analyst report), confirm- correlation between innovative behavior and ing the hypothesis that different complexity characteristic levels of complexity.

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Decision-making. Once information has been version of the stock market game in which the found and perceived, how is it used in decisions? criterion for productivity reflected only simple In the tactical game, Streufert and Schroder information tracking and found that subjects (1965) and Streufert, Suedfeld, and Driver with low integrative complexity had an advan- (1965b) studied the use of information in de- tage. By contrast, in the Internation Simula- cisions by assessing the number of input units tion, where the criterion for performance that subjects integrate in a decision. For every generally requires subtle and complex informa- input (e.g., message), subjects indicated the tion processing, comparisons of high and low decisions to which it was relevant. The basic complexity systems, which were in blocs com- curvilinear pattern was again found in both of posed predominantly of their own complexity these studies. A rising input load first increased, level, showed that the complex systems were then decreased integration of information in more productive. Bloc membership was found decision-making. At all but extreme input con- to affect productivity strongly. Complex groups ditions, the more complex systems integrated far in blocs composed of predominantly simple more input than simpler systems. These data, groups showed depressed productivity, while along with the perceptual data already dis- simple groups in generally complex blocs cussed, clearly indicate that although simpler showed elevated performance. Average pro- systems may at times engage in more input ductivity in the Internation Simulation had a search (particularly of a simple, authority curvilinear relationship to input load, in keeping oriented variety) they usually do not integrate with the integrative complexity model. In sum- more information into their output. mary, it seems that only when the criterion Prolductivity. It might seem that since more reflects integrative complexity in output will complex systems engage in more complex tactics the model presented here be relevant. of information search, perceive more input, and use more information in decisions, they would Implications of Model for Management generally be more productive than simpler Three implications seem important for man- systems. This is not so. The critical issue here agement: is the criterion used to measure productivity. 1. The tasks involved in job assignments of If the criterion reflects behavior that demands individuals and groups should be matched to complex search, high levels of perception of the characteristic level of integrative complexity input, and complex integrations in decisions, of the incumbents. It would be unproductive then clearly more complex systems are at an to place a very low complexity person (or advantage. If on the other hand, the criterion group) in a task for which complex information is such that simpler search methods and the processing is the criterion for success. Such an use of limited information is required for achiev- incumbent might collapse under the pressure, ing the goal, then there may be no; difference try to reduce the load (via staffs), or redefine or even an advantage for simpler systems. the position which could have injurious reper- Some data from the stock market simulation cussions throughout the organization. Equally and Internation Simulation support these views. unproductive would be an assignment of a high In one version of the stock market game complexity person (or group) to a task which (Driver, 1960), there were two performance required simple, routine behavior for success. criteria: one simply required adherence to The results might be either total withdrawal simple information search, while the other re- to internal processes (day-dreaming) and a quired use of more complex information and consequent decrease in performance or efforts self-initiated activity. Although the results to complicate or involve the assignment, which were not always quite significant, the trends could impair performance and lead to other were clear: more complex systems did better organization strains. on the criterion requiring complex behavior, Matching of jobs and personnel complexity while less complex systems did better on the clearly requires measurement of both. Some criterion requiring simpler behavior. preliminary measures of job demands are being These findings were amplified by two studies developed and several measures of characteristic in which simple and complex criteria were used levels of integrative complexity are available separately. Tuckman (1964) used a different for individuals. Schroder et al. report two

This content downloaded from 67.242.27.73 on Thu, 17 Dec 2020 18:25:09 UTC All use subject to https://about.jstor.org/terms Driver and Streufert: INFORMATION-PROCESSING SYSTEMS 281 projective measures of personality complexity- levels of integrative complexity. This would the Impression Formation Test described above mean that the capacity of organizations to meet and the Paragraph Completion Measure. The problems requiring integratively complex sys- latter consists of a series of words such as tems is decreased. Often individual or group parents, roles, and others which the subject uses systems which have regressed are simply re- as a topic for a short paragraph expressing his placed. But this seems less efficient and more reaction to the topic. Both projective measures costly than rotating assignments or varying load have scoring manuals to establish a unified in such a way that system complexity is not scoring method, and with proper training, a decreased. Whether the optimal input level is rater can attain high reliability (.8-.95 inter- the same across systems is an unresolved rater reliability). problem. A more objectively scored test of complexity 3. A final intriguing problem concerns meth- is desirable, however, and Schroder et al. de- ods of enhancing integrative levels, if desired. scribe several psychometric techniques which Dember's (1960) concept that a "pacer" stimu- could be used to assess complexity in specific lus, just a little ahead of a system's optimal content areas. The authors are currently at input complexity, produces an increase in sys- work on two general objective measures of tem level and possibly optimal motivation is characteristic levels of integrative complexity. well worth testing in human systems. Careful One is nearly complete and is being evaluated planning might enable an organization to pro- in a variety of experimental, educational, and vide just that amount of input above a person's industrial settings. This test-the Purdue- optimum to obtain a gradual increase in integra- Rutgers Experience Inventory I-has extremely tive complexity levels. It should be clear, high internal consistency (e.g., its factor struc- however, that for some criteria, increasing com- ture in two separate analyses has reflected the plexity of systems is not desirable. theoretically expected relationships among items A PHASIC THEORY very well) and has good item reliability (.4- .75). Its validity as a predictive instrument is The model presented was oversimplified in being ascertained in current research. Prelim- order to obtain some rough preliminary chart- inary results of one analysis show this test is ings in what is largely an unexplored area. It related strongly to established measures of was conveniently assumed that all parts of an teacher classroom style. This test is applicable information-processing system function in syn- to business management settings but the second chrony and that all parts follow a basically test, the Integration Style Test, which deals curvilinear pattern; however, it is already clear with business problems should prove even more that some forms of information search do not useful in such settings. The Integration Style fit such a pattern. A closer look at the data Test has proven reliable across several content suggests a phasic model of information process- areas of business decisions and also reveals via ing. factor analysis a theoretically meaningful in- A basic postulate in the phasic model is that ternal structure. there are at least two subsystems in an informa- 2. A second implication is that for any group tion-processing system. One subsystem is or individual, one should maintain the optimum termed the perceptual subsystem and is largely input range if maximum output is desired. This concerned with data search and intake. Intake implies the use of even nonfunctional informa- in a system is that set of processes which code, tion inputs or emotional inputs (e.g., slight interrelate, evaluate (especially in relation to threats or moderate rewards) to maintain maxi- need for immediate action) and store input. mal levels in systems if input is too low; also, This subsystem transmits processed input to the it implies the need for periodic relief from over- second subsystem-the executive subsystem, load positions (possibly by job rotation, vaca- which translates input into action decisions, tions, etc.). The problem is that if systems are actions, and strategies. For the individual allowed to decline because of low input level or system, this concept has already been developed if they are constantly overloaded (e.g., certain by Pribram (1958) in his analysis of brain executives) they are in danger of a permanent functioning. Pribram suggests that the brain regression to less and less complex characteristic operates in terms of two anatomically distinct

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HIGH

Perceptual Subsystem Complexity _ *-s-s _ Perceptual Subsystem Total Output Quantity ? ~ ~ ~ ~ ~ ~ = Executive Subsystem Complexity o go 0 0 0 0 0 0 Executive Subsystem Total Output Quantity _ go

z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~a0*

0~~~~~~~~~~~~~~~~~~~~~~~~~~~g

0 -j 4 < o- 0

CL~~~~~~~~NIOMNA INPU COMPLEXIT

FIGURE 3. INTEGRATIVE COMPLEXITY OR OUTPUT QUANTITY OF PERCEPTURAL AND EXECUTIVE

SUBSYSTEMS IN RELATION TO ENVIRONMENTAL INPUT COMPLEXITY.

intrinsic information-processing systems. The (research groups) or input intake (executive frontal system appears to process planning, secretaries), whereas other segments are con- decisions, and action, while the posterior cerned with executive output functions (sales system handles perceptions and judgments. or production managers). Judgment refers to the assignment of input The next basic postulate in the phasic model to evaluative categories (e.g., I like x); is that the two (or more) subsystems of an perception refers to the assignment of input to information-processing system are not syn- reality description or relational categories (e.g., chronous in their response to input. Both sub- x is red; x causes y). Conversely, decisions systems may respond to increasing load in a involve the integration of perceived inputs and series of interdependent phases, as shown in stored behaviors to attain some objectives Figure 3. Phase 1 represents the period in (based on judgments of the worth of various which both perceptual and executive subsystems goals). Plans are sets of behaviors developed increase in integrative complexity as conditions to implement decisions. move from relatively low input to moderate It is possible to extend Pribram's notions to input. In this phase the perceptual system may groups and organizations as well, always keep- reach its normal peak level of complexity. Some ing in mind that individual and group systems data (Schroder et al., 1967) suggest that some- are comparable only as information-processing times the perceptual system peaks at lower systems. In many other significant ways, bio- input values than the executive system (which logical and social systems may be quite dif- peaks in phase 3), while at other times their ferent. Nevertheless, as with individuals, one peaks coincide. The difference in peaking may can examine groups and often find segments of be due to the filtering action of the perceptual the group system focusing on information search subsystem, which does not transmit all input

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received and so reduces input for the executive complex executive outputs begins to fall as the subsystem. complexity of the executive subsystem declines; Phase 2 represents data (Chapman et al., while simple output continues to rise resulting 1959) which suggests that at moderate inputs, in a continued rise in total output. Finally in exceptionally complex integrations of perceptual phase 6, even simple outputs begin to decline, information may occur, which raise the percep- hence total output declines, as the information- tual subsystem to supernormal levels. If one processing system reaches exhaustion-energy, assumes only a limited capacity in energy (re- resources, and even processing units are worn sources) or processing units in a system, the out at described in the classic work on stress by cost of this supernormal perception may be a Selye (1956). drop in the energy or capacity of the executive It should be noted that whereas perceptual system, resulting in a temporary drop in its complexity decreases from phase 3 on, the complexity. amount of input perceptually processed and Phase 3 shows a decline in the integrative transmitted (with no complex additions from complexity of the perceptual subsystem, which memory) steadily increases, despite increasing begins to omit input altogether (Quastler and omissions. An alternate model might be that Wulff, 1955; Miller, 1960; 1962). However, in some systems the rate of omission of percep- if the rate of input omission in the perceptual tual input or the loss of information due to the subsystem is assumed to be less than the rate of failure of the perceptual subsystem to perform new imput, the executive subsystem still re- complex judgments or perceptions exceeds the ceives more input than in phase 2 and is stimu- rate of increase in input. This effect could lated to attain its peak complexity. Suppose, produce states of low input in the executive for instance, input rate corresponding to phase subsystem, which would produce phase 1-3 type 2 is 10 units per time unit. As this input rate action in the executive subsystem even under increases to 20, it may force an overload decline very high inputs. in the perceptual subsystem from transmission It should be evident that this is only a pre- of 100% of input to 80%. Futhermore, the liminary sketch of a potentially more complex amount of stored information added to input information-processing model which could be and thus transmitted to the executive subsystem applied to both simple and complex systems. (i.e., integratively complex perception) will Clearly systematic research is needed to evalu- also decline. Even so, the perceptual subsystem ate each phase of the proposed sequence. For will transmit 80% of 20 inputs (equals 16 in- instance, the phasic model might give one a puts) to the executive subsystem, whereas at very precise indication of how much input will lower input levels only 10 inputs were trans- produce an above-optimal perceptual response mitted. Thus, despite lower capacity in the (i.e., phase 2) rather than an optimal executive perceptual subsystem, the executive subsystem response (i.e., phase 3). At an organizational may still receive a greater load than in phase 2 level it may be found that some individuals and so attain maximum complexity in this excel in perceptual data handling functions, phase. while others excel in the executive functions. In phase 4, under still greater input, it ap- These possibilities are being explored. pears that the executive subsystem may at first It is hoped that these models and studies can maintain its integrative complexity and put out provide a useful perspective on the problems of a constant number of integratively complex management science. It is recognized that many decisions, while meeting further input increases factors, some of which may seem crucial, have with a rise in simple outputs (Streufert, Driver, not been dealt with, but this is a preliminary and Hahn, 1967). Thus, while the number of attempt at a synthesis of information-processing complex decisions stays constant, reflecting a approaches to management. steady state of subsystem complexity (dashed line in Figure 3), the number of simple decisions increases resulting in an increase in the total Michael J. Driver is associate professor of number or quantity of outputs from the execu- management at the University of Southern Cali- tive subsystem (dotted line in Figure 3). How- fornia. Siegfried Streufert is associate professor ever, in phase 5, the number of integratively of psychology at Purdue University.

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