TY - CHAP
T1 - System-Scientific Methods
AU - Huang, Linan
AU - Zhu, Quanyan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In Chap. 2, we briefly introduce essential system-scientific tools for modeling, analyzing, and mitigating cognitive vulnerabilities and cognitive attacks. Decision theory in Sect. 2.1 provides a scientific foundation of making decisions for single agents with different rationality levels under stochastic environments. Game theory is introduced in Sect. 2.2 to model the strategic interactions among multiple agents under several basic game settings and their associated Nash Equilibrium (NE) solution concepts. To address the challenges of incomplete information in decision-making and game modeling, we present two learning schemes in Sect. 2.3. These tools provide a system-scientific perspective to evaluate and reduce uncertainty in HCPSs, as illustrated by the blue and red lines in Fig. 2.1, respectively. We refer the readers to the notes at the end of each section for recent advances and relevant references.
AB - In Chap. 2, we briefly introduce essential system-scientific tools for modeling, analyzing, and mitigating cognitive vulnerabilities and cognitive attacks. Decision theory in Sect. 2.1 provides a scientific foundation of making decisions for single agents with different rationality levels under stochastic environments. Game theory is introduced in Sect. 2.2 to model the strategic interactions among multiple agents under several basic game settings and their associated Nash Equilibrium (NE) solution concepts. To address the challenges of incomplete information in decision-making and game modeling, we present two learning schemes in Sect. 2.3. These tools provide a system-scientific perspective to evaluate and reduce uncertainty in HCPSs, as illustrated by the blue and red lines in Fig. 2.1, respectively. We refer the readers to the notes at the end of each section for recent advances and relevant references.
KW - Bayesian learning
KW - Cumulative prospect theory
KW - Expected utility theory
KW - Game theory
KW - Nash equilibrium
KW - Reinforcement learning
KW - Von Neumann–Morgenstern utility theorem
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U2 - 10.1007/978-3-031-30709-6_2
DO - 10.1007/978-3-031-30709-6_2
M3 - Chapter
AN - SCOPUS:85161859214
T3 - SpringerBriefs in Computer Science
SP - 27
EP - 39
BT - SpringerBriefs in Computer Science
PB - Springer
ER -