4 Session 3: Bayesian Network

4.1 내용정리

  • Introduction

    • “A Bayesian network consists of a graphical structure and a probabilistic descrip- tion of the relationships among variables in a system. The graphical structure explic- itly represents cause-and-effect assumptions that allow a complex causal chain linking actions to outcomes to be factored into an articulated series of conditional relationships” (Borsuk, Stow, & Reckhow., 2004, p. 219).
    • Because of these links between actions and outcomes, social scientists can generate predictive results and develop network structure among variables beyond traditional social scientific approaches to increase the power of analysis. Conditional independence is at the core of Bayesian networks (Pe’er, 2005).
    • For the current example, we will look at situations where a number of people are working together in a complex environment. A number of these situations like Military training or Firefighting are potentially dangerous and costly. The develop- ment of new technology such as games provides us an opportunity to train people in a safer environment. Because of technological development, we can make these training simulations very close to real world actions. We use the term multiteam system (MTS) to describe nested teams engaged in military or firefighting opera- tions.

4.2 생각정리 (미팅내)

  • 한국의 HRD 많은 변화를 겪는 중

  • 매일매일 느끼는 감정과 정서를 수집

  • 머신러닝 - 옵티마이제이션이 특징

  • 잡 크레프팅

    • 긍정적인 영향들만 전이가 될 것인가
    • 어떤 팀(팀특성) 어떤팀에서는 좋은영향 어떤팀은 안좋은 영향
  • 통제적인 상황에서의 컨디션, 의사결정에 유용해 보임

  • 현업적용 포인트 AC센터

    • 조직문화진단

      • 심리적 안정감 -저,고 심리적 안정감 팀

      • 각 팀의 그룹프로세스, 실험

        • 회식, 팀 빌딩, 서프라이즈 파티 플레닝 등에서 행동을 수집

4.3 더 읽어볼 자료