Describing the Dutch Social Networks and Fertility Study and how to process it

BACKGROUND The social networks of people play a prominent role in theories on fertility. Investigating how networks shape behaviour is hard, because of the difficulty in measuring (large) networks among representative samples. Therefore, comprehensive studies of the variation in the structure and composition of networks and their impact on fertility outcomes are lacking. OBJECTIVE I aim to, first, describe the Dutch Social Networks and Fertility Study, and, second, describe the R-package FertNet that processes data from this study and transforms it into an easy-to-use format for researchers. METHODS The data used are from the Longitudinal Internet Social Survey (LISS) panel, a representative panel of Dutch households. The focus is on the Social Networks and Fertility Study that includes a subsample of women between the ages of 18‒40. Specific survey software was designed to capture each respondent’s personal network comprising 25 individuals with whom they had a relationship. In total, 758 women reported on over 18,750 relationships. For each person with whom the respondent had a relationship, several questions were asked about fertility-related topics. Uniquely, the connections between these people were also assessed. The R-package FertNet corrects data issues and transforms unstructured network data into alter-attribute and alter-tie datasets that can be handled by a diversity of network analytical approaches. CONTRIBUTION The Social Networks and Fertility Study is a unique resource that allows for a comprehensive investigation of how networks shape fertility behaviour. It provides better estimates of network characteristics than earlier literature based on smaller networks. The R-package FertNet assists researchers in their analyses.</p

A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach

Gated networks: an inventory

Gated networks are networks that contain gating connections, in which the outputs of at least two neurons are multiplied. Initially, gated networks were used to learn relationships between two input sources, such as pixels from two images. More recently, they have been applied to learning activity recognition or multi-modal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while adopting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applications of these networks, as well as recent extensions to the basic architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page

Collecting large personal networks in a representative sample of Dutch women

In this study we report on our experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. We made use of GENSI, a recently developed tool for network data collection using interactive visual elements that has been shown to reduce respondent burden. A sample of 758 women between the ages of 18 and 40 were recruited through the LISS-panel; a longitudinal online survey of Dutch people. Respondents were asked to name exactly 25 alters, answer sixteen questions about these alters (name interpreter questions), and assess all 300 alter-alter relations. Nearly all (97%) respondents reported on 25 alters. Non-response was minimal: 92% of respondents had no missing values, and an additional 5% had fewer than 10% missing values. Listing 25 alters took 3.5 ± 2.2 (mean ± SD) minutes, and reporting on the ties between these alters took 3.6 ± 1.3 min. Answering all alter questions took longest with a time of 15.2 ± 5.3 min. The majority of respondents thought the questions were clear and easy to answer, and most enjoyed filling in the survey. Collecting large personal networks can mean a significant burden to respondents, but through the use of visual elements in the survey, it is clear that it can be done within reasonable time, with enjoyment and without much non-response

Policy Search in Continuous Action Domains: an Overview

Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, providing a unified perspective on very different approaches, including also Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches.Comment: Accepted in the Neural Networks Journal (Volume 113, May 2019

How might life history theory contribute to life course theory?

In this commentary, we consider how evolutionary biology’s life history theory (LHT) can be integrated with life course theorizing, to the benefit of both endeavors. We highlight areas where it can add value to existing work in life course theory (LCT), focusing on: how it can add an extra level of explanation, which may be helpful in understanding why individuals focus on their own health and happiness (or why they don’t); how insights from comparative work, both across species and across all kinds of human populations, can inform LCT; and how social and biological researchers can come together fruitfully to make progress on the tricky issue of understanding human agenc

Balancing bias and burden in personal network studies

Personal network data is increasingly used to answer research questions about the interplay between individuals (i.e., egos) and their social environment (i.e., alters). Researchers designing such data collections face a trade-off: When eliciting a high number of alters, study participation can be particularly burdensome as all data is obtained by surveying the ego. Eliciting a low number of alters, however, may incur bias in network characteristics. In the present study we use a sample of 701 Dutch women and their personal networks of 25 alters to investigate two strategies reducing respondent burden in personal network data collections: (1) eliciting fewer alters and (2) selecting a random subsample from the original set of elicited alters for full assessment. We present the amount of bias in structural and compositional network characteristics connected to applying these strategies for every possible network size (2–24 alters) as well as the potential study time savings as a proxy for respondent burden reduction. Our results can aid researchers designing a personal network study to balance respondent burden and bias in estimates for a range of compositional and structural network characteristics