Cultural Integration and Differentiation in Groups and Organizations

Experimental and field research has demonstrated a pervasive tendency toward pairwise conformity among individuals connected by positive social ties, and work using formal models has shown that opinions on connected inuence networks should thus converge toward uniformity. Observing that diversity persists even in small scale groups and organizations, we investigate two empirically grounded mechanisms of social differentiation that may account for this persistence: First, actors may dislike or disrespect peers who diverge too much from their own views, and may change their opinions or behaviors to distance themselves further from those negative referents. Second, when surrounded by similar others, actors may try to maintain a sufficient sense of uniqueness by exploring new opinions or behaviors. Using computational experiments, we demonstrate that these two mechanisms lead to different patterns of polarization, radicalization, and factionalism and also investigate theconditions under which integration occurs. In closing, we discuss the implications for cultural dynamics in organizations

Problem-Oriented Conceptual Model and Ontology for Enterprise e-Recruitment

Internet-led labour market has become so competitive forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. The research is motivated by the results of a failed e-recruitment project as a case study by focusing on the difficulty of scoping and representing recruitment problem knowledge to systematically inform the RE process towards an e-recruitment solution specification. In this paper, a Problem-Oriented Conceptual Model (POCM) supported by an Ontology for Recruitment Problem Definition (Onto-RPD) for contextualisation of the enterprise e-recruitment problem space is presented. Inspired by Soft Systems Methodology (SSM), the POCM and Onto-RPD are produced based on the detailed analysis of three case studies: (1) Secureland Army Enlistment, (2) British Army Regular Enlistment, and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). The POCM and the ontology are demonstrated and evaluated by a focus group against a set of criteria. The evaluation showed a valuable contribution of the POCM in representing and understanding the recruitment problem and its complexity

Monolingual Biases in Simulations of Cultural Transmission

Recent research suggests that the evolution of language is affected by the inductive biases of its learners. I suggest that there is an implicit assumption that one of these biases is to expect a single linguistic system in the input. Given the prevalence of bilingual cultures, this may not be a valid abstraction. This is illustrated by demonstrating that the ‘minimal naming game’ model, in which a shared lexicon evolves in a population of agents, includes an implicit mutual exclusivity bias. Since recent research suggests that children raised in bilingual cultures do not exhibit mutual exclusivity, the individual learning algorithm of the agents is not as abstract as it appears to be. A modification of this model demonstrates that communicative success can be achieved without mutual exclusivity. It is concluded that complex cultural phenomena, such as bilingualism, do not necessarily result from complex individual learning mechanisms. Rather, the cultural process itself can bring about this complexity

Minding morality : ethical artificial societies for public policy modeling

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Minding Morality: Ethical Artificial Societies for Public Policy Modeling

Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient dimensions of a culture is critically important for producing relevant and accurate evaluations of social policy when using multi-agent artificial intelligence models and simulations