# 👾 Cognition - 인지과학과 계산적 사고 > *"How do we think? How do we compute? How do we decide?"* ## Field Overview Cognition field는 **계산적 인지과학(Computational Cognitive Science)**과 **의사결정 이론**을 중심으로, 인간과 조직이 어떻게 생각하고 학습하며 결정하는지 연구합니다. ### Core Questions - How do entrepreneurs recognize opportunities? (인지적 편향과 발견) - How can we model learning and adaptation? (베이지안 학습) - What are the resource constraints on rational decision-making? (제한된 합리성) - How do we formalize problem-solving? (계산적 접근) ### Key Journals - **Cognitive Science** - **Psychological Review** - **Organization Science** (cognition focus) - **Management Science** (behavioral operations) ## Research Themes ### 1. 🧠 Computational Rationality **Resource-rational decision making under constraints** Related papers: - [[📜👾_bhui21_optimize(decisions, resources)]] - Resource optimization in decisions - [[📜👾_gershman15_compute(rationality, resources)]] - Computational rationality framework - [[📜👾_peng21_overload(information, decisions)]] - Information overload effects **Core idea**: Human cognition adapts to computational constraints --- ### 2. 🎲 Bayesian Cognition **How minds grow and learn through probabilistic inference** Related papers: - [[📜👾_tenanbaum11_grow(minds, cognition)]] - Theory of mind growth - [[📜Goodman07_learning_caus]] - Causal learning - [[📜tenanbaum14_1sample(1decide)]] - One-shot learning **Core idea**: Human learning as Bayesian inference --- ### 3. 🎯 Entrepreneurial Cognition **How entrepreneurs think differently** Related papers: - [[📜👾_busenitz97_recognize(entrepreneurs, biases)]] - Cognitive biases in entrepreneurs - [[📜👾_camuffo19_structure(experiments, learning)]] - Structured experimentation - [[📜👾_stern24_model(beliefs, experimentation)]] - Belief updating through experiments **Core idea**: Entrepreneurs as hypothesis testers --- ### 4. 🔬 Experimental Learning **Learning through experimentation and failure** Related papers: - [[📜👾_gans23_choose(entrepreneurship, experimentation)]] - Experimentation in entrepreneurship - [[📜👾_vul14_one_done]] - One and done learning - [[📜👾_march91_extract(organizations, small-histories)]] - Learning from small samples **Core idea**: Small samples, big lessons --- ### 5. 🤖 AI and Formalization **Automating reasoning and problem formulation** Related papers: - [[📜👾_mansinghka25_automate(formalization, programming)]] - Automated formalization - [[📜👾_xuan24_plan(instruction, cooperation)]] - AI-human cooperation - [[📜ullman20_conceptualdev]] - Conceptual development **Core idea**: AI as cognitive collaborator --- ## Methodology Focus ### Experimental Design - Behavioral experiments - A/B testing - Field studies ### Computational Modeling - Bayesian inference - Reinforcement learning - Agent-based models ### Statistical Methods - Hierarchical models - Causal inference - Meta-analysis ## Writing Guide for 👾 Cognition Papers ### Structure 1. **Problem**: What cognitive puzzle are we solving? 2. **Theory**: What computational/cognitive model explains it? 3. **Experiment**: How do we test the theory? 4. **Results**: What did we learn about how minds work? 5. **Implications**: How does this change our understanding? ### Language - Use **cognitive constructs**: beliefs, attention, memory, learning - Emphasize **mechanisms**: "how" over "what" - Bridge **theory and data**: model → prediction → test ### Common Mistakes ❌ Pure description without mechanism ❌ No formal model or computational framework ❌ Ignoring cognitive constraints ❌ Missing the "so what" for decision-making ## Related Fields - [[1🐢Innovation]] - Innovation as cognitive search - [[3🐙Operations]] - Behavioral operations - [[2🐅CompBayes]] - Bayesian inference methods ## Key Papers by Theme ![[papers.base#papers-cog]] --- > *"The mind is not a perfect reasoner, but a bounded one that adapts to its environment and constraints"*