Modeling sociocultural influences on decision making: by Joseph V. Cohn, Sae Schatz, Hannah Freeman, David J. Y.

By Joseph V. Cohn, Sae Schatz, Hannah Freeman, David J. Y. Combs

In our more and more globally interconnected international, knowing and appreciating the sociocultural context during which participants make their judgements is important to constructing profitable partnerships. the gathering of chapters during this quantity illustrates how advances in info and social media applied sciences, in addition to modeling and simulation instruments, mixed with the social sciences, might be leveraged to higher know the way sociocultural context affects selection making.

The chapters during this quantity have been contributed by means of major specialists from academia, undefined, and govt and provide:

  • Insights into cross-cultural choice making according to contemporary overseas occasions, with grounding in an ancient context
  • Discussions of state of the art modeling thoughts used at the present time via pros throughout a number of firms and agencies
  • Descriptions of particular cross-cultural choice making instruments designed to be used through laypeople and pros
  • Case stories at the position of cross-cultural determination making grounded in present occasions and (in many circumstances) army applications.

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Extra resources for Modeling sociocultural influences on decision making: understanding conflict, enabling stability

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Coalition forces will face high uncertainty about the urban terrain, often unpredictable populations, and limited tactical visibility and maneuverability. They will also face limitations in weapon employment (due to concerns about collateral damage) and degraded communications that hinder command and control. Cities may harbor adversarial actors ranging from criminals to terrorists seeking a safe haven in urban concealment. Today, understanding the complex dimensions of unique dense urban areas often requires many human experts from different organizations pooling their knowledge and whatever data they have.

18 Modeling Urban Environments ............................................................................................. 18 Modeling Populations ............................................................................................................. 19 Example: Athena ...................................................................................................................... 20 Composing Models ..................................................................................................................

2014). The condition of roads observed from an overhead imagery might yield clues about the economic conditions in a neighborhood (Owen, 2014). OSINT including social media can complement ISR and help fill in the gaps. For example, open source information can provide insight on demographics and building purposes/ occupants. Open sources include published monographs, reports, websites, commercially available data sets, news, and social media. These include text, images, video, and structured databases.

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