Modeling method of combat mission based on OODA loop

Today, modern warfare has shifted from weapon-centric operations to network-centric system operations, which has led to a linear increase in the complexity of combat missions. When a commander faces a high-level mission, how to model the entire combat mission is a critical step, and it is also the basis for the generation of subsequent combat plans and combat command and control. Aiming at this problem, this paper proposes a task modeling method based on the OODA loop. This method first decomposes the mission task, and defines four meta tasks based on the OODA loop theory, and finally analyzes the meta tasks from the perspective of time and information. The mission relationship is defined, which can realize the modeling and formal description of the entire combat mission process, and provide support for the follow-up combat links.

User Retention: A Causal Approach with Triple Task Modeling

For many Internet companies, it has been an important focus to improve user retention rate. To achieve this goal, we need to recommend proper services in order to meet the demands of users. Unlike conventional click-through rate (CTR) estimation, there are lots of noise in the collected data when modeling retention, caused by two major issues: 1) implicit impression-revisit effect: users could revisit the APP even if they do not explicitly interact with the recommender system; 2) selection bias: recommender system suffers from selection bias caused by user's self-selection. To address the above challenges, we propose a novel method named UR-IPW (User Retention Modeling with Inverse Propensity Weighting), which 1) makes full use of both explicit and implicit interactions in the observed data. 2) models revisit rate estimation from a causal perspective accounting for the selection bias problem. The experiments on both offline and online environments from different scenarios demonstrate the superiority of UR-IPW over previous methods. To the best of our knowledge, this is the first work to model user retention by estimating the revisit rate from a causal perspective.

Focus on form in task repetition through oral and written task modeling

This study expands upon research into task repetition effects by exploring the effect of task modeling between the performances of the same oral narrative task and its extension to a new task. Seventy-one advanced beginner English as a foreign language (EFL) learners were divided into three groups: task repetition with oral modeling (TR + OM), task repetition with written modeling (TR + WM), and task repetition with no modeling (TR). All groups enacted another oral narrative task (a new task of the same type), three days apart. Participants’ oral narrative task performances were analyzed in terms of complexity, accuracy and fluency. Results revealed that TR + WM was more effective than the TR + OM, and both were significantly better than the control group in leading to subordination complexity in the repeated task and the new task. Whereas the percentage of error-free clauses remained unchanged over time, the accurate verb forms increased in TR + WM’s repeated task, but declined in the new task. Fluency in terms of articulation speed and mid-clause silent pauses was improved and maintained in the new task in all groups, with both the experimental groups particularly the TR + OM outperforming the TR regarding the significant reduction of repair in the repeated and new task performances. In conclusion, these significant developments induced by the modeling conditions speak to the strength of models in providing and extending linguistic features beyond learners’ current repertoires. Pedagogical implications of these findings are discussed.

Retest Reliability of Integrated Speed–Accuracy Measures

Cognitive tasks borrowed from experimental psychology are often used to assess individual differences. A cardinal issue of this transition from experimental to correlational designs is reduced retest reliability of some well-established cognitive effects as well as speed–accuracy trade-off. The present study aimed to address these issues by examining the retest reliability of various methods for speed–accuracy integration and by comparing between two types of task modeling: difference scores and residual scores. Results from three studies on executive functions show that (a) integrated speed–accuracy scoring is generally more reliable as compared with nonintegrated methods: mean response time and accuracy; and (b) task modeling, especially residual scores, reduced reliability. We thus recommend integrating speed and accuracy, at least for measuring executive functions.

Punishment and corruption

Punishment is one of the main methods for preventing corruption. However, studies on the effect of size and probability of punishment on bribe-taking have not yielded conclusive results. We introduce a punishment by a fine or termination of the task, both with varying probabilities, in a laboratory task modeling the decision to take a bribe. The punishment decreased the probability of taking higher bribes, even though the probability of taking lower bribes was unaffected. Participants took fewer bribes when the fine was larger and more probable. We did not observe any clear negative effects of small punishment crowding out intrinsic motivation to behave honestly. However, we found that effects of punishment differ based on emotionality and honesty-humility of participants. The study shows that the prospect of punishment may deter dishonest behavior; however, personality characteristics should be taken into account when devising an effective deterrence policy.

Experimental test of the effects of punishment probability and size on the decision to take a bribe

Punishment is one of the main methods for preventing corruption. However, studies on the effect of size and probability of punishment on bribe-taking have not yielded conclusive results. We introduce a punishment by a fine or termination of the task, both with varying probabilities, in a laboratory task modeling the decision to take a bribe. The punishment decreased the probability of taking higher bribes, even though the probability of taking lower bribes was unaffected. Participants took fewer bribes when the fine was larger and more probable. We did not observe any clear negative effects of small punishment crowding out intrinsic motivation to behave honestly. However, we found that effects of punishment differ based on emotionality and honesty-humility of participants. The study shows that the prospect of punishment may deter dishonest behavior; however, personality characteristics should be taken into account when devising an effective deterrence policy.