Upper-level Units Research Articles

Social restructuring during harsh environmental conditions promotes cooperative behaviour in a songbird.

Cooperation may emerge from intrinsic factors such as social structure and extrinsic factors such as environmental conditions. Although these factors might reinforce or counteract each other, their interaction remains unexplored in animal populations. Studies on multilevel societies suggest a link between social structure, environmental conditions and individual investment in cooperative behaviours. These societies exhibit flexible social configurations, with stable groups that overlap and associate hierarchically. Structure can be seasonal, with upper-level units appearing only during specific seasons, and lower-level units persisting year-round. This offers an opportunity to investigate how cooperation relates to social structure and environmental conditions. Here, we study the seasonal multilevel society of superb fairy-wrens (Malurus cyaneus), observing individual responses to experimental playback of conspecific distress calls. Individuals engaged more in helping behaviour and less in aggressive/territorial song during the harsher non-breeding season compared to the breeding season. The increase in cooperation was greater for breeding group members than for members of the same community, the upper social unit, comprised of distinct breeding groups in association. Results suggest that the interaction between social structure and environmental conditions drives the seasonal switch in cooperation, supporting the hypothesis that multilevel societies can emerge to increase cooperation during harsh environmental conditions.

Cognitive Edge Computing–Based Fault Detection and Location Strategy for Active Distribution Networks

This article proposes a fault detection and location strategy based on cognitive edge computing to harvest the benefits of cognitive edge computing and address the special needs of active distribution networks (ADNs). In the proposed strategy, an ADN smart gateway is used to compile data in a central repository where it will be processed and analyzed. The intermediary smart gateway includes a protection unit where the fault detection, location, and isolation are accomplished through a combination of virtual mode decomposition (VMD), support vector machine (SVM,) and long short-term memory (LSTM)–type deep machine learning tools. The local measurements of branch currents and bus voltages are processed through VMD, and the informative decomposed components are provided as inputs to the SVM-based fault detection unit and LSTM-based fault location unit. The smart digital relay passes trip commands to the respective circuit breaker/s and submits compiled data regarding the history of faults and protection actions to the upper-level units. The findings from simulation results demonstrate the effectiveness of the proposed strategy to provide fast and accurate fault detection and protection against all types of faults and locations in the ADN.

A case study in the use of audio-visual essays for university screen and media assessment

ABSTRACT This article critically reflects on the use of audio-visual essays for assessment in undergraduate Screen and Media courses at Flinders University. Since 2018, audio-visual essays have been set as an optional assessment task in first-year and upper level units. In 2019, an audio-visual essay was set as a compulsory assessment item in the first-year course SCME1000 Film Form and Analysis. This was supported with a student-run audio-visual essay lab, in which upper-level Screen and Media students and recent graduates instructed first-year students in basic editing techniques for the audio-visual essay. These labs provided an opportunity for skilled upper-level students and recent graduates to engage with the first-year cohort, impart their skills, model good practice, and generated possibilities for future mentoring and collaboration. Students and demonstrators were subsequently surveyed and interviewed to reflect on the value of their experience working with audio-visual essays. Drawing on this data set, the assessment of the students’ audio-visual essays is charted against the unit of study’s stated educational aims and expected learning outcomes. In the process, this article considers the value of using audio-visual essays when designing assessment for a large, mixed cohort of students with diverse learning styles, skillsets, and professional ambitions.

Multilevel Analysis with Few Clusters: Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference

AbstractQuantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by usingrestrictedML estimators for variance parameters and at-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.

Theorien von Kapitalismusvarianten und Wohlfahrtsregimen: Annahmen, Erfolge und die Notwendigkeit anderer Methoden

This article reviews assumptions, contributions, and impasses of the two most important theories of capitalist diversity: Esping-Andersen’s (1990) welfare regime and Hall and Soskice’s (2001) varieties of capitalism typology. It shows that each theory implies a multilevel structure that nests lower-level units in upper-level units. However, even though this multilevel structure is at the center of many debates in the comparative capitalism literature, it is rarely explicitly modeled, let alone tested. Identifying this as an important route forward for research on capitalist diversity, I will show how future research could employ multilevel models to answer some of the most important questions about capitalist diversity.

Generic Demand Model Considering the Impact of Prosumers for Future Grid Scenario Analysis

The increasing uptake of residential PV-battery systems is bound to significantly change demand patterns of future power systems and, consequently, their dynamic performance. In this paper, we propose a generic demand model that captures the aggregated effect of a large population of price-responsive users equipped with small-scale PV-battery systems, called prosumers , for market simulation in future grid scenario analysis. The model is formulated as a bi-level program in which the upper-level unit commitment problem minimizes the total generation cost, and the lower-level problem maximizes prosumers’ aggregate self-consumption. Unlike in the existing bi-level optimization frameworks that focus on the interaction between the wholesale market and an aggregator, the coupling is through the prosumers’ demand, not through the electricity price. That renders the proposed model market structure agnostic, making it suitable for future grid studies where the market structure is potentially unknown. As a case study, we perform steady-state voltage stability analysis of a simplified model of the Australian National Electricity Market with a significant penetration of renewable generation. The simulation results show that a high prosumer penetration changes the demand profile in ways that significantly improve the system loadability, which confirms the suitability of the proposed model for future grid studies.

Three-Level Longitudinal Mediation with Nested Units: How does an Upper-Level Predictor Influence a Lower-Level Outcome via an Upper-Level Mediator Over Time?

ABSTRACTIn the study, extending from the cross-lagged panel model (CLPM) and the 2-2-1 cross-sectional multilevel mediation model, we proposed a three-level longitudinal mediation model to evaluate the causal process among variables at different levels over time. Given the complexity of the proposed model, Bayesian estimation was used. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for the proposed model. Factors considered in the simulation study included average sample sizes of the lower-level units within each upper-level unit (or cluster size; ), numbers of upper-level units (or clusters; J), numbers of time points (T), fixed direct and indirect effect sizes, and variances and covariances of upper-level random effects. Results indicated that the fixed effect estimates for the indirect effect components (a and b), fixed direct effect (c), and the average mediation effect estimate were reasonably accurate when , J > 20, and T ⩾ 4. Comparing the proposed model against the CLPMs coupled with aggregation or disaggregation techniques, we found that the CLPMs yielded misleading fixed-effects estimates and low coverage rates when variation existed between upper-level units. The proposed model was illustrated using a study on early childhood education. We compared estimates and inferences from the proposed three-level longitudinal mediation model, the CLPMs with aggregation and disaggregation, and the traditional 2-2-1 mediation model. Limitations and extensions of the current study were discussed.

The Costs of Simplicity: Why Multilevel Models May Benefit from Accounting for Cross-Cluster Differences in the Effects of Controls

Context effects, where a characteristic of an upper-level unit or cluster (e.g., a country) affects outcomes and relationships at a lower level (e.g., that of the individual), are a primary object of sociological inquiry. In recent years, sociologists have increasingly analyzed such effects using quantitative multilevel modeling. Our review of multilevel studies in leading sociology journals shows that most assume the effects of lower-level control variables to be invariant across clusters, an assumption that is often implausible. Comparing mixed-effects (random-intercept and slope) models, cluster-robust pooled OLS, and two-step approaches, we find that erroneously assuming invariant coefficients reduces the precision of estimated context effects. Semi-formal reasoning and Monte Carlo simulations indicate that loss of precision is largest when there is pronounced cross-cluster heterogeneity in the magnitude of coefficients, when there are marked compositional differences among clusters, and when the number of clusters is small. Although these findings suggest that practitioners should fit more flexible models, illustrative analyses of European Social Survey data indicate that maximally flexible mixed-effects models do not perform well in real-life settings. We discuss the need to balance parsimony and flexibility, and we demonstrate the encouraging performance of one prominent approach for reducing model complexity.

Consequences of Unmodeled Nonlinear Effects in Multilevel Models

Applications of multilevel models have increased markedly during the past decade. In incorporating lower-level predictors into multilevel models, a key interest is often whether or not a given predictor requires a random slope, that is, whether the effect of the predictor varies over upper-level units. If the variance of a random slope significantly differs from zero, the focus of the analysis may then shift to explaining this heterogeneity with upper-level predictors through the testing of cross-level interactions. As shown in this article, however, both the variance of the random slope and the cross-level interaction effects may be entirely spurious if the relationship between the lower-level predictor and the outcome is nonlinear in form but is not modeled as such. The importance of conducting diagnostics to detect nonlinear effects is discussed and demonstrated via an empirical example.

Lower level mediation in multilevel models.

Multilevel models are increasingly used to estimate models for hierarchical and repeated measures data. The authors discuss a model in which there is mediation at the lower level and the mediational links vary randomly across upper level units. One repeated measures example is a case in which a person's daily stressors affect his or her coping efforts, which affect his or her mood, and both links vary randomly across persons. Where there is mediation at the lower level and the mediational links vary randomly across upper level units, the formulas for the indirect effect and its standard error must be modified to include the covariance between the random effects. Because no standard method can estimate such a model, the authors developed an ad hoc method that is illustrated with real and simulated data. Limitations of this method and characteristics of an ideal method are discussed.

HIERARCHICAL DYNAMIC OPTIMIZATION FOR LINEAR DISCRETE SYSTEMS

In this paper an on-line multilevel suboptimal control procedure is developed for large-scale, time-invariant, and discrete linear systems with quadratic performance criterion, and measurement disturbances having arbitrary statistical distributions. The on-line coordination procedure as reported here employs an upper-level intervention through interaction estimates and goal modifications in order to modify lower-level subsystem optimization problems. Their individual controls are applied to the system and the resulting output is estimated by Kalman Type filter and performance information is fedback to the upper level unit. A reference control approach is used to up-date intervention variables over a sequence of time instants to improve on the control being applied to the process. In the computer simulation of the process, it was found that the actual overall cost for systems using this on-line multilevel control procedure is within ±10% of the cost resulting from a conventional overall optimal control policy.

An On-Line Coordination Approach for a Class of Large Linear Multilevel Systems

An on-line multilevel suboptimal control proced-are is developed in this paper for large-scale time-invariant discrete linear systems with arbitrary input and measurement disturbances, and a quadratic performance criterion. The procedure employs upper-level intervention through interaction estimates and goal modifications for the purpose of modifying lower-level, subsystem optimization phoblems. Individual controls are applied and the resulting output is estimated by a Kaiman Type filter. The performance information is fed back to the. upper level unit and a reference control is used to up-date. the. intervention variables over a sequence of time instants. From simulation studies it was found that the overall performance cost for systems using this on-line multilevel control procedure is within ±10% of the performance cost resulting from a conventional overall optimal control policy.