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3 Verbal Conceptual and Cultural Models I n this chapter we discuss models that are not instantiated in formal algorithms or software, but in words. Verbal conceptual models are presented first, followed by verbal cultural models.1 These models are important for their attempts to apply theoretical constructs to the behavior of individuals and groups. The models and the terms and constructs they encompass may provide a foundation for some of the more applied and formal models discussed later. These models have been developed in the dis- ciplines of social psychology, sociology, anthropology, and organizational behavior studies. vERBAL CONCEPTuAL MODELS What Are verbal Conceptual Models? Verbal conceptual models characterize entities, variables, or events/ processes/mechanisms and the relations among them in words, not in equa- tions or other mathematical or operational formulations. Although they may use mathematical terms—for example, Kurt Lewin’s statement that all behavior is a “function” of the person and the situation (1951)—the nature 1 Note that, in general, both conceptual models and cultural models can be articulated in formal logical, mathematical, algorithmic, or computational forms. Our focus in this chapter is on verbal representations of conceptual and cultural models, as an initial stepping stone to- ward computational implementations in individual, organizational, and societal (IOS) models and simulations. 

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 BEHAVIORAL MODELING AND SIMULATION and form of relations described in a verbal model are commonly under- specified compared with formal models. Verbal conceptual models include very general classifications or broad characterizations that provide the foundation for a new discipline, such as the brain-as-computer metaphor on which modern cognitive science was founded, and mid-level frameworks, such as “images” of organizations (Morgan, 1997) as machines, or organ- isms, or brains. Typologies or taxonomies, such as a taxonomy of emotional states (Borgatti, 1994) or a typology of small groups (Arrow, McGrath, and Berdahl, 2000), are another form of verbal conceptual model. Most numer- ous of all are the small-scale models that characterize relations among vari- ables or processes relevant for understanding a specific phenomenon. The “progression of withdrawal” and “compensatory behaviors withdrawal” models, for example, are alternate models of job withdrawal (quitting and absenteeism) (Hanisch, 2000); another example is a two-variable model of how social norms emerge in a newly formed group, based on whether or not new members’ characterizations of the situation and the “scripts” they retrieve to guide behavior match (Bettenhausen and Murnighan, 1985). The use of such terms as “theory,” “framework,” “model,” and “paradigm” in psychology and the social sciences is as informal as the models themselves.2 One person’s conceptual model is another person’s theory or framework. In this chapter, we use the term “conceptual model” (and, for brevity, sometimes just “model”) as a way to group theories, frameworks, and paradigms into rough classification systems based on common features in structure (for example, dual process models, dynamic models, threshold models) or relevant domain (group development models, organizational withdrawal models, visual attention models). What verbal conceptual models have in common is that they tend to be “highly infor- mal constructions, use the natural language system, are rich in metaphor, and use lavishly nuanced statements” (Davis, 2000, p. 218). If rendered as diagrams instead of in straight prose, they tend to be represented via two- by-two tables, labeled boxes with arrows drawn between them, or perhaps a flowchart for a process model. In psychology and the social sciences, theorizing about a problem typi- cally begins with verbal conceptual models, which then may be elaborated and adjusted over time as relevant empirical data accumulate. Formal mathematical models, computational models, statistical models, etc. rely on verbal conceptual models to specify variables and relations among them, although a host of extra assumptions and plausible estimates are typically needed to translate a verbal theory into a workable implementation. Hence 2 This is also true in the behavior modeling and simulation community, which is why we attempted in Chapter 1 to identify and differentiate four levels of representation: theory, ar- chitecture (here, framework), model, and simulation (here, paradigm).

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 VERBAL CONCEPTUAL AND CULTURAL MODELS a computational model of emotional response relies on a conceptual tax- onomy of emotional states (Ekman and Davidson, 1995), and a process simulation model of jury decision making, such as DISCUSS (Stasser, 1988), relies on the guiding metaphor of “interacting minds” engaged in “col- lective information processing” (which represents the mind-as-computer metaphor generalized to groups-as-networked-computers). State of the Art for verbal Conceptual Models Sophisticated verbal conceptual models (whose authors often call them theories) are typically more specific about the nature of relations among variables or about the nature of processes described than are ad hoc models or global metaphor models. They may also be more sophisticated in incorporating contingencies, dynamics, and multiple levels of analysis. For example, in the study of leadership effectiveness, a very simple model, the leadership grid (Blake and Mouton, 1982), proposes that leadership effec- tiveness is explained by two dimensions—concern for people and concern for production—and the more a leader has of both, the better. This model focuses entirely on the leader (single level), entertains no contingencies, assumes linear additive components, and has no dynamic elements. A more sophisticated model, situational leadership theory (Hersey and Blanchard, 1988), proposes that the optimal mix of task-oriented and relation-oriented behavior by leaders depends on the level of maturity and corresponding skill level of the subordinate, which is expected to change over time. In a heterogeneous group of members at different levels, effective leadership will require that the leader tailor her style to individual members and adjust that style as each member progresses through four successive levels of maturity and autonomy. This model incorporates three levels (individual, dyad, and group), contingencies (different levels of member development), and change over time. Computational models are often used to model complex processes that unfold over time, so verbal conceptual models that include attention to dynamics are particularly useful as a resource for the implementation of more formal models. Verbal conceptual models of groups and organizations can be arrayed along a continuum of increasing complexity using the four different levels of complexity in time research of Ofori-Dankwa and Julian (2001).3 First-level models focus on mean differences in, for example, how much time a process takes and assume stationarity (sometimes implicitly rather than explicitly). Second-level models add change as a possibility, so that the rate of a process may speed up or slow down across time. Third- 3 The following summary is adapted from Arrow, Henry, Poole, Wheelan, and Moreland (2005, pp. 313–368).

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00 BEHAVIORAL MODELING AND SIMULATION level models incorporate more than one hierarchical level of a system. For example, the rate of change at the member, small group, and organizational levels may be expected to differ systematically. Fourth-level models allow for multiple simultaneous and potentially nonstationary processes at dif- ferent levels. Zaheer, Albert, and Zaheer (1999) introduced the concept of “time- scale completeness” for a process model. In essence, they define what a process model needs to specify to provide sufficient guidance in designing a research program. Although their focus was on empirical data collection, the same desiderata apply for implementing a verbal model in a compu- tational form. A theory is time-scale complete if it specifies time scale for all of its variables, relationships, and boundary conditions. For example, it needs to specify the time needed for a complete instance of the phenomenon to occur, the nature and rate of change in variables, and the duration and sequence of any subphases in the process. Otherwise researchers cannot make theory-driven choices of observation, recording, and aggregation intervals, and the criteria for evidence either in support of or contrary to model predictions remain unclear. Finally, state-of-the-art conceptual models allow for conceptual “dock- ing” with other models by clarifying how the terms used relate to other, closely related (or synonymous) terms in the literature, and note where other models might “plug in” (for example, a structural model might refer to possible plug-in models that address processes or mechanisms not included in but relevant to the structural model) and clearly specifying boundary conditions. Relevance to Modeling Requirements One way to demonstrate the relevance of verbal models is to give an example of how a well-developed verbal conceptual model could be used for rapid cultural awareness training. The conceptual model is the cross- cultural framework of Fiske (1991, 2000), which proposes that human beings in all cultures coordinate their social interactions using a mix of fun- damental relational models: communal sharing, authority ranking, equal- ity matching, and market pricing. The four models are organized sets of associated concepts and rules that serve as a generative grammar for think- ing about and coordinating relationships.4 When following the communal sharing model, people emphasize the common identity of group members and focus on what is good for the group as a whole. The preferred model of decision making is consensus and people pool resources and draw on the pool without keeping track of individual contributions and withdrawals. 4 The following summary is adapted from Arrow and Burns (2004, pp. 176–178).

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0 VERBAL CONCEPTUAL AND CULTURAL MODELS Prototypical contexts and domains in which this model is used are family and food. Families commonly share food resources freely, and people who are defined as the “in group” in a particular context (such as invited guests at a party) are expected to help themselves to whatever food and drink they want. Violations of the rules occur when out-group members attempt to access in-group resources (for example, someone crashes a party). In relationships organized by the authority ranking model, people structure their interactions according to status, position, and dominance hierarchy. Military organizations commonly use this model, and personnel wear insignias of rank to signal status. How people behave is strongly governed by whether they have the higher or lower rank of the two people in a given interaction. In distributing resources, high-status members get more, and low-status members get less. Rank also comes with obligations: superiors are expected to provide for or take care of inferiors. Violations occur when lower status members are insubordinate, treating a higher ranked person as an equal, for example, or when higher status members abuse their rank and power and betray their obligations to lower status followers or dependents. When a relationship is governed by the equality matching model, people reciprocate favors after some delay and maintain a balance between giving and receiving. This model is commonly applied among people who consider themselves to be of equal status, such as friends, classmates, or colleagues. People in equality matching relationships often respond to favors by saying “I owe you one” or “I’ll pay next time.” Note the difference from authority ranking, in which a lower status person responds to favors with gratitude and loyalty, rather than reciprocating in kind. If the relationship is using the equality matching model, however, the failure to reciprocate a favor (or to express one’s understanding of this obligation) would be a violation. In market pricing relationships, people seek the best deal for themselves and expect that others will do the same. This model commonly governs trade and other social exchanges among strangers or acquaintances and is guided by the equity principle of proportionality—so that price, for example, should be proportional to value. Self-interested or selfish behavior is not a violation (it is expected), but cheating or stealing (which violates the equity rule) is. Particular cultural implementations of these models organize social exchange, distribution, contribution, decision making, social influence, moral judgment, aggression, and conflict (Fiske, 1991). There is no prac- tical limit, for example, to the types of objects or services that might be deemed appropriate or inappropriate to reciprocate the gift of a chicken or a radio. This sort of idiosyncratic and culturally specific content can be pro- vided only by an informant who is very familiar with a culture. However, more important to practical application in a field situation is simply detect-

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0 BEHAVIORAL MODELING AND SIMULATION ing whether the context in which a chicken is received is one that calls for gratitude and acknowledgment of an in-group bond (communal sharing), expression of deference and humility (authority ranking), reciprocation with a favor of roughly equal value (equality matching), or direct payment (market pricing). Mistakes involving specific cultural content (reciprocating with an odd sort of gift) may invoke humor or surprise; violation of rela- tional models (paying someone for a gift, failing to reciprocate) are more likely to give offense and damage the relationship. Major Limitations The strengths of verbal models are also their weakness. Natural lan- guage is a flexible and nuanced instrument in which one can express highly sophisticated ideas, including multiple overlapping metaphors and embed- ded narratives. However, because natural language is an encompassing sea in which we all swim, shadings of meaning and idiosyncratic clouds of associations allow four people to encounter the “same” verbal model and understand it in four different ways. Some of the ambiguities and gaps that are common in verbal models may become evident when designing an experiment, and they are highlighted most sharply when one attempts to extract a set of formal relations from a natural language model. In psychology and the social sciences, the grand metaphors of con- ceptual models often govern the whole direction of a field, but meta- phors always direct attention to some features and lead to the neglect of others. Once a broad conceptual framework such as this becomes perva- sive, scholars tend to forget that a metaphor is involved. For example, the information-processing metaphor for the brain, and the researchers who focused on it, probably contributed to a pervasive neglect of research on emotional and social processes for the first several decades of cognitive sci- ence.5 Computers don’t have emotions and are not social beings. So if the mind is not simply like a computer in some ways (simile with boundary conditions), but is a biological computer (unreflective metaphor), the ways in which minds are decidedly not like computers get overlooked, even by researchers engaged in intensively social activities about which they have strong feelings. The same curious social blindness is evident in the early era of organizational research dominated by the organization-as-machine metaphor. The related notion that workers are cogs in the machine led researchers to study the impact of physical conditions, such as lighting, on worker productivity while completely ignoring the possible impact of one human being on another (Mayo, 1960). 5Asmall pocket of researchers clearly forged ahead in this important area; see, for example, Ortony et al. (1988).

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0 VERBAL CONCEPTUAL AND CULTURAL MODELS verification and validation Issues Verbal conceptual models are sometimes specific enough that they can be tested and plausibly falsified, using empirical field studies or controlled experiments. For example, in studies of subjects from Bengali, Chinese, Korean, Vai (Liberia and Sierra Leone), and U.S. cultures (Fiske, 1992), Fiske and colleagues have used social cognition experiments to demonstrate that people organize acquaintances in memory according to the dominant model that organizes the relationship and that for many subjects this clas- sification accounts for more variance in recall and substitution errors than such personal attributes as gender, race, and age. In contrast to such well-developed conceptual frameworks, broad meta- phors (brains as information-processing devices, organizations as cultures) are not really subject to verification or falsification. Whether or not they are used in a particular domain is likely to depend largely on face validity and established precedent. In evaluating the usefulness of a verbal model of this nature, the yardstick is often not how well supported the model is, but how much interesting research it inspires. Even when a verbal model seems, in principle, to be subject to falsification, the underspecification of relations and processes often means that a rather broad array of different outcomes can be presented as “consistent with” the theory. As Harris (1976) noted in “The Uncertain Connection Between Verbal Theories and Research Hypotheses in Social Psychology,” theoretical terms often are not defined, boundary conditions are unspecified, and, under various plausible interpre- tations of assumptions or conditions, several well-known theories include internal contradictions and inconsistencies (as cited in Davis, 2000). Future Research and Development Requirements Verbal conceptual models can be highly influential and generative and do not require intensive funding or technology to develop, yet the develop- ment of such models is often overlooked as a funding priority. The scarce resource in improving verbal theory is intellectual time and energy. Moti- vation may also be an issue when grant funding is available primarily for doing (conducting experiments, writing code, designing games, collecting reams of data) and not for thinking. This can encourage the proliferation of low-level, poorly specified, ad hoc conceptual models that get spawned in discussion sections of journal articles to explain the results of a single set of studies and, if they survive, are later herded together in introduction sections of subsequent articles without actually being systematically inte- grated into more comprehensive integrated models. That work is generally left for the writers of literature reviews who are trying to make sense of a mountain of facts and ideas and find a deeper order.

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04 BEHAVIORAL MODELING AND SIMULATION Stronger theory is needed for domains that social scientists still don’t know how to think about and those in which numerous weak conceptions have not been integrated. Verbal conceptual models are essential building blocks for theory building. Bringing people together for conferences and funding edited books and special issues that explore themes and issues in depth are useful. Measurable advances in theory should also be specified as a valuable deliverable for grants. Think tanks could be funded for scholars to come together and work intensively for an extended period (three to six months) on theory development and integration for issues and areas in which it is increasingly clear not only that there are not enough data, but also that it is difficult to know how to conceptualize the problem. Of course this sort of conversation is going on in labs and institutes around the coun- try, but the focus on generating data (at least in psychology) seems to eclipse or marginalize the systematic development and integration of theory that goes beyond the highly specific area in which people tend to do research. CuLTuRAL MODELINg What Is Cultural Modeling? The term “cultural modeling” encompasses two broadly different areas of research. One area is concerned with modeling growth and distribu- tion of cultural phenomena, such as the evolution of norms or the diffu- sion of beliefs. Research in this tradition typically treats culture (or, more accurately, some characteristic of culture) as an outcome and concentrates on the factors shaping those outcomes. This kind of cultural modeling is distinguished from other kinds of modeling surveyed in this volume only by the domain of study—namely, an element of culture. It does not imply a particular modeling technique. For example, the evolution of norms may be studied using a variety of methods, including multivariate statistics, agent-based models, system dynamics models, event history models, and so on. This kind of cultural modeling is discussed in several chapters in this volume and is not discussed further here. The other kind of cultural modeling, which is discussed here, is con- cerned with describing (and often formally representing) a group’s culture. Work in this tradition typically does not concern itself with how the culture came to be but rather with how it is distributed in the population and, in the best cases, what the consequences of having that culture might be. Finally, it is appropriate to note that perhaps the most fundamental verbal cultural models are those that are implicit in a region’s or society’s language and history. It is abundantly clear—from Laurence of Arabia’s exploits to today’s attempts to “democratize” Iraq—that deep and broad knowledge of the local history and language are still fundamental for the

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05 VERBAL CONCEPTUAL AND CULTURAL MODELS kind of high-level understanding of societal dynamics that is the main focus of this report and of today’s military. This committee acknowledges the importance of both language and history as the foundational knowledge base for any cultural model development, and as perhaps the starting point for identifying “implicit” models embedded in the language and history— models that can be built on in successive formalization efforts. What Is Culture? Culture can be defined in a number of different ways. Indeed, over 200 scholarly definitions have been documented (Kroeber and Kluckhohn, 1952). Researchers have defined culture in normative, historical, biological, cognitive, functional, structural, categorical, and symbolic terms. Defini- tions typically make use of some combination of the following elements: beliefs, behaviors, values, customs, artifacts, organizational orientations, preferences, experiences, attitudes, meanings, hierarchies, religions, percep- tions, conceptions, material objects, possessions, symbols, motives, tradi- tions, strategies, ideals, rules, habits, reasoning, identities, conventions, customs, and institutions, among others. While definitions of cultures differ on which of these elements constitute culture, most view a group’s culture as an essential factor in problem solving, coping, and adapting to envi- ronmental changes. In addition, they generally agree that culture is some- thing possessed by groups (such as societies, organizations, occupations, teams) and that it is learned, transmitted, and shared (albeit imperfectly and unevenly). At the same time, scholars regard culture as being held in individual minds and do not consider it an oxymoron to talk about an individual’s culture. State of the Art of Culture Models There are four basic types of descriptive culture models popular today: cultural inventory models, dominant trait models, semantic models, and cultural domain models. Cultural Inventory Models Cultural inventory models are a way of describing cultures by list- ing which of a list of traits they do or do not possess. Thus cultures are conceived of as distinctive bundles of features that can be represented as a string of 1s and 0s indicating the presence or absence of a trait. A number of anthropologists have undertaken a compilation of cultural traits across human societies. The best and most relevant example of such a compilation across cultures is the Standard Cross-Cultural Survey developed by George

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0 BEHAVIORAL MODELING AND SIMULATION Murdock and others. The database consists of 186 societies and 22 cultural categories involving almost 1,000 standard coded variables derived from ethnographic sources (Murdock and Morrow, 1970). Essentially, a team of researchers has combed through ethnographies written by anthropologists and coded the cultures described using a universal codebook. The ability to compare features across societies is critical for both developing models and testing theories concerning patterns of and associations among cultural traits, categories, and features. Table 3-1 provides examples of some of the 22 cultural categories and associated variables and their codes. For military purposes (McFate, 2005), many of these traits may be irrelevant, while others would need to be gathered, such as information on cultural gestures (e.g., meaning of certain hand gestures), cultural greeting etiquettes (e.g., rules for properly entering a village), cultural norms sur- rounding conflict (e.g., cultural notions of courage, honor, and revenge), etc. Such a database would considerably improve the ability to interact in a satisfactory manner with natives and to accurately predict their reactions to stimuli. The key difficulty with cultural inventories is obtaining the necessary data. Data need to be collected on an ongoing basis to ensure the quality and timeliness of information. Also, it is important to recognize cultural boundaries and subcultures. For example, a nation like China may form a single political unit but may contain many different cultures. Furthermore, collecting new cultural information can be particularly difficult during periods of conflict, which means that the data need to be collected on an ongoing basis regardless of whether it has immediate utility. Another approach is the cultural classification system developed by Karabaich, which is intended to cover the possible group types that might be encountered in a military, business, or political context (Karabaich, 2004). These group types are summarized in Table 3-2. TABLE 3-1 Examples of Cultural Categories and Coded Variables from the Standard Cross-Cultural Survey Examples of Cultural Categories Examples of Labels for Variables Within Categories Subsistence economy Marital residence and supportive practices Matrilocal or uxorilocal—with wife’s kin Avunculocal—with husband’s mother’s brother’s kin Patrilocal or virilocal—with husband’s kin Ambilocal—with either wife’s or husband’s kin Neolocal—separate from kin

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0 VERBAL CONCEPTUAL AND CULTURAL MODELS TABLE 3-1 Continued Examples of Cultural Categories Examples of Labels for Variables Within Categories Political organization Political power—most important source Direct subsistence production Warfare wealth Tribute or taxes Slaves Contributions of free citizens Large landholdings Political office Foreign commerce Capitalistic enterprises Priestly services Cultural complexity Fixity of residence Nomadic Seminomadic Semisedentary Sedentary, impermanent Sedentary Sexual attitude and Frequency of premarital sex—male practice Universal Moderate Occasional Uncommon Relative status of Mythical founders of the culture women All male Both sexes, but the role of men more important Both sexes, and the role of both sexes fairly equal Both sexes, but female role more important, or solely female Cultural theories of Theories of soul loss illness Absence of such a cause Minor or relatively unimportant cause An important auxiliary cause Predominant cause recognized by the society Female power and male Female economic control of products of own labor dominance Absent Present Political decision Conflict between communities of the same society making and conflict Endemic: high physical violence, feuding, and/or raiding occur regularly Moderately high, often involving physical violence Moderate: disputes may occur regularly but tendency to manage them in a more or less peaceful manner Mild or rare Nature of warfare Value of war: violence/war against nonmembers of the group Enjoyed and considered to have high value Considered to be a necessary evil Consistently avoided, denounced, not engaged in SOURCE: Adapted from Murdock and Morrow (1970).

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 VERBAL CONCEPTUAL AND CULTURAL MODELS TABLE 3-4 Findings Regarding Cultural Differences in Human Inference: Inductive Reasoning (ability to generalize from limited data) (Hudlicka, 2004) Category of Inference Findings Covariation Ji, Peng, and Nisbett (2000) judgment Chinese versus Americans (identifying Simple stimuli presented on computer screen correlations Chinese more confident about judgments between cues) Chinese more correct in judgments Chinese showed no primacy effect Americans showing strong primacy effect “East Asian cognition has been held to be relatively holistic; that is, attention is paid to the field as a whole. Western cognition, in contrast, has been held to be object focused and control oriented. In this study East Asians (mostly Chinese) and Americans were compared on detection of covariation and field dependence. The results showed the following: (a) Chinese participants reported stronger association between events, were more responsive to differences in covariation, and were more confident about their covariation judgments; (b) these cultural differences disappeared when participants believed they had some control over the covariation judgment task; (c) American participants made fewer mistakes on the Rod-and-Frame Test, indicating that they were less field dependent; (d) American performance and confidence, but not that of Asians, increased when participants were given manual control of the test” Causal Miller (1984) attribution Americans versus Hindu Indians (identifying Fundamental attribution error evident in Americans causal Hindu Indians attribute behavior to social roles, obligations, physical relations environment between cues) Attributed to different beliefs regarding causality (content difference) Social Morris, Nisbett, and Peng (1995) Americans versus Chinese Fundamental attribution (mass murderers, computer animations of fish) Americans attributed behavior to individual dispositions Chinese attributed behavior to environment Lee, Hallahan, and Herzog (1996) Americans and Hong Kong Chinese Sportswriters’ descriptions of events American writers focus on individuals Hong Kong writers focus on situational factors Nisbett (2003); Jones and Harris (1967) Americans and Koreans Judgment of another person’s attitude Americans assume due to disposition Koreans assume due to contextual influences continued

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 BEHAVIORAL MODELING AND SIMULATION TABLE 3-4 Continued Category of Inference Findings Causal Asian folk physics is relational, emphasizing fields and force over distance attribution Western folk physics focuses on nature of object itself, rather than its Physical relation to the environment Peng et al. (2001, p. 252) Peng and Knowles (2003) Chinese versus Americans Force-over-distance explanations (aerodynamic, hydrodynamic, magnetic) Americans referred more to nature of object Chinese referred more to the field Person Chiu, Hong, and Dweck (1997) perception Hong Kong Chinese versus Americans Judgment of self as fixed versus changing Americans assume fixed, enduring traits Chinese assume changing self Peng et al. (2001) Chinese versus Americans Type of information used in person perception judgments Americans focused on evidence provided by target Chinese focused on evidence provided about the target by others Inference of Americans prefer “what you see is what you get” norm of authenticity mental states Asians would consider this impolite Knowles, Morris, Chiu, and Hong (2001) Chinese versus Americans Judgment of mental states (thoughts, feelings, desires) Americans: focus on what “they say” Chinese: focus on what “they don’t say” Categorization General findings: • Some categories are more stable across cultures than others. Examples of stable categories are: basic emotions, colors, basic shapes • Westerners tend to categorize objects by color at an early age and by function later. Africans tend to use color throughout their life. (This finding may be related to formal education more than culture.) • More cultural influence for goal-based categories than for environment- based categories • More salient categories for a given culture are more highly differentiated (culture directs attention) • Culture determines types of features used in defining categories • Asians may be less attuned to categories in their inferences and category learning • Some evidence that Asians tend to use relational features as basis for categorization • Differences in “chronic accessibility” (less for Koreans than for Americans) • Possible differences in category acquisition (exemplar based versus rule based) • Self-descriptions (Americans in terms of fixed traits; Asians in terms of roles; more “socially diffused”) SOURCE: Hudlicka (2004, Table 3.2.2-1 from Psychometrix Technical Report 0412, pp. 28–30).

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 VERBAL CONCEPTUAL AND CULTURAL MODELS TABLE 3-5 Findings Regarding Cultural Differences in Human Inference: Deductive Reasoning Category of Inference Findings Syllogisms Luria (1931, Russia); Cole (1996, Africa) Subjects did not engage syllogistic problems at the theoretical level (i.e., if asked to deduce something based on a presented syllogism, they would frequently think out of the box and suggest that the experimenter go find out for himself; why would x be true, etc.) Real-world (culturally relevant) grounding of topic makes a large difference in success on task Dialectical Asians: changing nature of reality and enduring presence of reasoning contradictions versus Western: linear epistemology built on notions of truth, identity, and noncontradiction Resolving contradiction: Chinese seek compromise; Americans seek exclusionary (either-or) truth and resolution (Peng and Nisbett, 1999) Assumption in Eastern dialectical epistemology: • Principle of change—everything is always in flux (thus x may not be identical with itself because it may change over time) • Principle of contradiction—opposing qualities coexist • Principle of holism—everything is linked to everything else and isolating phenomena may lead to misleading conclusions • Folk wisdom: greater frequency and preference for dialectical (apparently contradictory proverbs) among Chinese than Americans Social Americans tended to blame one side versus Chinese tended to see fault contradictions/ in both conflicts SOURCE: Hudlicka (2004, Table 3.2.2-2 from Psychometrix Technical Report 0412, p. 31). behavior prediction, the most significant differences are those in emotion elicitation; that is, in the specific situations and stimuli triggering particular emotions. Variations were found both in the nature of the emotions elicited and the intensity of those emotions. Some of these findings are summarized in Table 3-6. Semantic Models Semantic models are not researcher-based models but rather the models that ordinary people use to understand their worlds. The models are often tacit, in the sense that individuals are not aware they have them. Anthro- pologists discover the models by interviewing people and listening to their accounts of daily life. They typically consist of chains of prototypical events

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4 BEHAVIORAL MODELING AND SIMULATION TABLE 3-6 Differences in Emotion Elicitors Across Cultures: Summary of Findings Situation Emotion Elicited Birth of new family member More intense joy for Europeans/Americans than Japanese Body-centered basic pleasures More intense joy for Europeans/Americans than Japanese Achievement More intense joy for Europeans/Americans than Japanese; more fear for Americans Death of loved one More frequent triggers of sadness for Europeans/Americans than Japanese Physical separation from a More frequent triggers of sadness for Europeans/Americans loved one than Japanese World news More frequent triggers of sadness for Europeans/Americans than Japanese Strangers More frequent trigger of anger for Japanese than for Europeans/Americans; more fear for Americans Novel situations More fear for Japanese Negative developments in More sadness for Japanese than Europeans/Americans relationships SOURCE: Hudlicka (2004, Table 3.2.2-3 from Psychometrix Technical Report 0412, p. 33). that constitute plans of action. D’Andrade defined these sorts of models as “a cognitive schema that is intersubjectively shared by a social group” (D’Andrade, 1989, p. 809). Semantic models are qualitative or conceptual rather than computational models. As an example of a semantic model, Naomi Quinn (1987) has analyzed hundreds of hours of interviews to discover concepts underlying American marriage and to show how these concepts are tied together. She began by looking at patterns of speech and at the repetition of key words and phrases, paying particular attention to informants’ use of metaphors and the commonalities in their reasoning about marriage. For example, one of her informants said that “marriage is a manufactured product.” This metaphor paints marriage as something that has properties like strength and staying power and as something that requires work to produce. Some marriages are “put together well,” while others “fall apart” like so many cars or toys or washing machines (Quinn, 1987, p. 174).

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5 VERBAL CONCEPTUAL AND CULTURAL MODELS The objective is to look for metaphors in rhetoric and deduce the schemas, or underlying principles, that might produce patterns in those metaphors. Quinn found that people talk about their surprise at the breakup of a marriage by saying that they thought the couple’s marriage was “like the Rock of Gibraltar” or that they thought the marriage had been “nailed in cement.” People use these metaphors because they assume that their listeners know that cement and the Rock of Gibraltar are things that last forever (i.e., they are intersubjectively shared). Quinn reasons that if schemas or scripts are what make it possible for people to fill in around the bare bones of a metaphor, then the metaphors must be surface phenomena and cannot themselves be the basis for shared understanding. Quinn found that the hundreds of metaphors in her corpus of texts fit into just eight linked classes that she calls lastingness, shared- ness, compatibility, mutual benefit, difficulty, effort, success (or failure), and risk of failure. For example, Quinn’s informants often compared marriages (their own and those of others) to manufactured and durable products (“it was put together pretty good”) and to journeys (“we made it up as we went along; it was a sort of do-it-yourself project”). Quinn sees these metaphors, as well as references to marriage as “a lifetime proposition,” as exemplars of the overall expectation of lastingness in marriage. Other examples of the search for cultural schemas in texts include a study of the reasoning that Americans apply to interpersonal problems (Holland, 1985), a study of ordinary Americans’ theories of home heat control (Kempton, 1987), and a study of what chemical plant workers and their neighbors think about the free enterprise system (Strauss, 1997). Cultural Domain Analysis Cultural domain analysis refers to perspectives on and methods for analyzing culture drawn from cognitive anthropology (Borgatti and Everett, 1992). A cultural domain is a collection of items that in some sense go together or are all examples of a kind of x (e.g., animals, plants). Such domains are often linguistic categories (e.g., semantic domains or concepts) in that there is a simple name for the set of items, like fruit or vegetables. What makes these domains cultural is that they are consensual. There is general agreement on the part of cultural actors regarding membership of most items in the domain. However, like all human things, the boundaries of a domain can be porous or fuzzy. There are items that are clearly in the domain, and items that are clearly outside, and many items that are in- between. The general objective of this type of analysis and modeling is to understand the cultural domain, which means to know what items belong in it and how these items are perceived to relate to one another (i.e., the extent to which they are similar or different). The data are collected and

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 BEHAVIORAL MODELING AND SIMULATION analyzed in a systematic manner using data collection techniques, such as pile sorts, sentence completion tasks, and triads tests (similar methods are referred to as repertory grid analysis in psychology; see Johnson and Weller, 2002), and analytical methods, such as hierarchical clustering and multi- dimensional scaling, to identify the conceptual organization and shared dimensions among concepts. Analysis of domain items can also include their attributes (e.g., diseases and their symptoms). A good example of a general principle stemming from this form of analysis comes from Stefflre (1972) in his proposition that people will behave similarly toward things they perceive as being similar. The importance of this approach lies in the ability to quickly assess the nature of cultural beliefs and conceptions, albeit for a rather narrowly delineated set of cultural items. However, such an understanding can facili- tate the ability to alter or change cultural beliefs and ultimately human behavior. These types of methods have been used in consumer research for both product development and marketing (Stefflre, 1972). Johnson, Griffith, and Murray (1987) and Murray, Griffith, and Johnson (1987), for example, have used this approach in changing people’s beliefs about underutilized fish species, leading to increased consumption of fish that were traditionally considered “trash” fish. Another branch of cultural domain analysis is the cultural consensus model (CCM) of Romney, Weller, and Batchelder (1986). The model origi- nated as a theoretical exploration of the formal conditions under which similarity of beliefs would imply cultural knowledge. It was shown that, in the context of a true/false questionnaire asking respondents to react to propositions of fact, the degree of knowledge of each respondent could be inferred when three conditions held. First, that a single culturally correct right answer exists that is valid for all respondents in the sample. Second, that conditional on the underlying cultural answer key, the responses of subjects are independent (i.e., when they did not know the answer to a question, their responses were uncorrelated). Third, that the questionnaire contained questions about only one domain of knowledge (that is, a single competence level for each person sufficed to characterize their probability of answering any question correctly). When these three conditions held, the model was capable of deriving both the culturally correct answer key and the cultural competence of each respondent. The model allows for a test of the degree to which cultural knowledge is shared, who has more or less of this cultural knowledge, and how it varies among a group of people in terms of, for example, gender, levels of human capital, and social class. It also allows for the construction of the culturally correct answers by working backward via Bayesian statistical techniques from the pat- terns of agreement concerning a series of related cultural propositions or statements.

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 VERBAL CONCEPTUAL AND CULTURAL MODELS This approach has a number of advantages in terms of understanding and modeling culture, particularly with respect to modeling aspects of intra- cultural variation.6 CCM has been used in a variety of contexts, but it has been applied practically to solving policy and management issues, model- ing indigenous ecological knowledge, and understanding people’s cultural beliefs concerning various aspects of health and illness. It has recently been used to measure cultural consonance (i.e., the correspondence between cultural beliefs and actual behavior) that has been shown to correlate with health outcomes (e.g., low consonance is related to high blood pressure; see Dressler and Bindon [2000]). The CCM approach can be used to empirically determine shared beliefs and knowledge that can be used in models incorporating cultural variables. In addition, the approach can also be used to more finely tune an under- standing of cultural beliefs and their variation that may be patterned in terms of different social attributes (e.g., gender, age). Thus, cultural knowl- edge (the correct cultural response) or individual cultural competency can be treated as either a dependent or an independent variable in a model at various levels of analysis. Relevance to Modeling Requirements and Major Limitations For the purposes of this study, a key limitation of all the models reviewed in this section is that they were not built for military purposes. The variables and dimensions they have focused on (such as power distance) have not been shown to be relevant for any given military situation. More generally, dif- ferent aspects of culture are relevant for different situations, and as a result a new model must be built for each substantially different military purpose and for each group of people (who have distinct cultures). Another limitation of these models is that they do not explicitly link culture and behavior and therefore do not provide direct guidance on how to intervene in a group in order to change the culture. A partial exception is cultural domain analysis, which posits that people behave similarly to 6 Understanding intercultural variations has benefited significantly from the approach taken by Heinrich et al. (2004), in which an economic “game” (such as the Ultimatum Game, Güth, Schmittberger, and Schwarze, 1982) is introduced across a number of different societies, and the resulting behaviors correlated (or “normalized”) with respect to the interaction pattern norms found in each society. As the authors note: “We draw two lessons from the experimental results: first, there is no society in which experimental behavior is even roughly consistent with the canonical model of purely self-interested actors; second, there is much more variation between groups than has been previously reported, and this variation correlates with differ- ences in patterns of interaction found in everyday life” (p. 5). Clearly there are implications for war games, understanding cultural biases with respect to aggression across cultures, and anticipating adversary tactics for a range of Department of Defense IOS modelers.

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 BEHAVIORAL MODELING AND SIMULATION similar stimuli. As a result, it is possible to predict that people’s behavior toward a new course of action will be similar to their reactions toward other courses of action that are similar. Another difficulty with predicting behavior is that the behavior of inter- est to predict may often be that of individuals. However, some models, such as the semantic models, unless they were based on a single individual, are not intended to apply to any single individual. Other models, such as the trait- based models of Hofstede, are based on individuals but then aggregated to the group level. Cultures are then described by the traits of the majority. Data, verification, and validation Issues Cultural inventory models rely on ethnographic observation and are therefore both time-consuming to develop and highly subjective. Having multiple independent observers helps ameliorate the subjectivity problem but is expensive. Dominant trait models, such as the Hofstede dimensional models, can involve two sets of data. The first set of data is used to derive the dimensions. These can be validated by a number of different statistical methods, such as factor analysis. Once these are fixed, another set of data is obtained to score each new culture on the dimensions. These data have to be obtained from willing natives of the culture, and the data have to be updated over time because cultures change. Future Research and Development Needs In a certain sense, cultural models are critical for all the computational models discussed in this volume, because the cultural models provide the principles to be embedded in those models. For example, an agent-based model of crowd behavior needs to know the cultural rules for behavior that will govern the agents’ interactions. The biggest limitation of cultural models at present is that existing models were not designed with military purposes in mind. As a result, a key research need is to develop models applicable to military needs. This would include semantic models of how natives think about land, nation, war, for- eigners, and so on, as well as cultural inventory models that include relevant variables. Note that different models are needed for different cultures. The semantic models are particularly powerful for military applica- tions. However, they are currently not formal models, meaning that they are expressed verbally and not in ways that are immediately amenable to computational analysis. As a result, another key research direction is to develop formal ways of expressing semantic models that are simple enough to be used by field researchers and subject matter experts.

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