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1. Strategic Evaluation

   https://doi.org/10.1145/3617694.3623237  

   Laufer, Benjamin; Kleinberg, Jon; Levy, Karen; Nissenbaum, Helen (October 2023, ACM)
2. Mate evaluation theory.

   https://doi.org/10.1037/rev0000360  

   Eastwick, Paul W.; Finkel, Eli J.; Joel, Samantha (January 2023, Psychological Review)

   There are two unresolved puzzles in the literatures examining how people evaluate mates (i.e., prospective or current romantic/sexual partners). First, compatibility is theoretically crucial, but attempts to explain why certain perceivers are compatible with certain targets have revealed small effects. Second, features of partners (e.g., personality, consensually-rated attributes) affect perceivers’ evaluations strongly in initial-attraction contexts but weakly in established relationships. Mate Evaluation Theory (MET) addresses these puzzles, beginning with the Social Relations Model postulate that all evaluative constructs (e.g., attraction, relationship satisfaction) consist of target, perceiver, and relationship variance. MET then explains how people draw evaluations from mates’ attributes using four information sources: (a) shared evolved mechanisms and cultural scripts (common lens, which produces target variance); (b) individual differences that affect how a perceiver views all targets (perceiver lens, which produces perceiver variance); (c) individual differences that affect how a perceiver views some targets, depending on the targets’ features (feature lens, which produces some relationship variance); and (d) narratives about and idiosyncratic reactions to one particular target (target-specific lens, which produces most relationship variance). These two distinct sources of relationship variance (i.e., feature vs. target-specific) address puzzle #1: Previous attempts to explain compatibility used feature lens information, but relationship variance likely derives primarily from the (understudied) target-specific lens. MET also addresses puzzle #2 by suggesting that repeated interaction causes the target-specific lens to expand, which reduces perceivers’ use of the common lens. We conclude with new predictions and implications at the intersection of the human-mating and person-perception literatures. 

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3. Multiagent Evaluation Mechanisms

   Alon, T.; Dobson, M.; Procaccia, A.; Talgam-Cohen, I; Tucker-Foltz, J. (January 2020, AAAI)
4. State-Aware Meta-Evaluation of Evaluation Metrics in Interactive Information Retrieval

   https://doi.org/10.1145/3459637.3482190  

   Liu, Jiqun; Yu, Ran (October 2021, 30th ACM International Conference on Information & Knowledge Management)

   In interactive IR (IIR), users often seek to achieve different goals (e.g. exploring a new topic, finding a specific known item) at different search iterations and thus may evaluate system performances differently. Without state-aware approach, it would be extremely difficult to simulate and achieve real-time adaptive search evaluation and recommendation. To address this gap, our work identifies users' task states from interactive search sessions and meta-evaluates a series of online and offline evaluation metrics under varying states based on a user study dataset consisting of 1548 unique query segments from 450 search sessions. Our results indicate that: 1) users' individual task states can be identified and predicted from search behaviors and implicit feedback; 2) the effectiveness of mainstream evaluation measures (measured based upon their respective correlations with user satisfaction) vary significantly across task states. This study demonstrates the implicit heterogeneity in user-oriented IR evaluation and connects studies on complex search tasks with evaluation techniques. It also informs future research on the design of state-specific, adaptive user models and evaluation metrics. 

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5. Aircraft Icing Severity Evaluation

   https://doi.org/10.3390/encyclopedia2010005  

   Li, Sibo; Paoli, Roberto (March 2022, Encyclopedia)

   Aircraft icing refers to the ice buildup on the surface of an aircraft flying in icing conditions. The ice accretion on the aircraft alters the original aerodynamic configuration and degrades the aerodynamic performances and may lead to unsafe flight conditions. Evaluating the flow structure, icing mechanism and consequences is of great importance to the development of an anti/deicing technique. Studies have shown computational fluid dynamics (CFD) and machine learning (ML) to be effective in predicting the ice shape and icing severity under different flight conditions. CFD solves a set of partial differential equations to obtain the air flow fields, water droplets trajectories and ice shape. ML is a branch of artificial intelligence and, based on the data, the self-improved computer algorithms can be effective in finding the nonlinear mapping relationship between the input flight conditions and the output aircraft icing severity features. 

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