How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.Comment: 9 pages, 3 figure

Privacy-preserving Inference of Group Mean Difference in Zero-inflated Right Skewed Data with Partitioning and Censoring

We examine privacy-preserving inferences of group mean differences in zero-inflated right-skewed (zirs) data. Zero inflation and right skewness are typical characteristics of ads clicks and purchases data collected from e-commerce and social media platforms, where we also want to preserve user privacy to ensure that individual data is protected. In this work, we develop likelihood-based and model-free approaches to analyzing zirs data with formal privacy guarantees. We first apply partitioning and censoring (PAC) to ``regularize'' zirs data to get the PAC data. We expect inferences based on PAC to have better inferential properties and more robust privacy considerations compared to analyzing the raw data directly. We conduct theoretical analysis to establish the MSE consistency of the privacy-preserving estimators from the proposed approaches based on the PAC data and examine the rate of convergence in the number of partitions and privacy loss parameters. The theoretical results also suggest that it is the sampling error of PAC data rather than the sanitization error that is the limiting factor in the convergence rate. We conduct extensive simulation studies to compare the inferential utility of the proposed approach for different types of zirs data, sample size and partition size combinations, censoring scenarios, mean differences, privacy budgets, and privacy loss composition schemes. We also apply the methods to obtain privacy-preserving inference for the group mean difference in a real digital ads click-through data set. Based on the theoretical and empirical results, we make recommendations regarding the usage of these methods in practice

Agent-based models as a tool for exploring complex segregation processes : simulating scenarios of residential segregation in the Helsinki Metropolitan Area

Tiivistelmä – Referat – Abstract With rising income inequalities and increasing immigration in many European cities, residential segregation remains a key focus for city planners and policy makers. As changes in the socio-spatial configuration of cities result from the residential mobility of its residents, the basis on which this mobility occurs is an important factor in segregation dynamics. There are many macro conditions which can constrain residential choice and facilitate segregation, such as the structure and supply of housing, competition in real estate markets and legal and institutional forms of housing discrimination. However, segregation has also been shown to occur from the bottom-up, through the self-organisation of individual households who make decisions about where to live. Using simple theoretical models, Thomas Schelling demonstrated how individual residential choices can lead to unanticipated and unexpected segregation in a city, even when this is not explicitly desired by any households. Schelling’s models are based upon theories of social homophily, or social distance dynamics, whereby individuals are thought to cluster in social and physical space on the basis of shared social traits. Understanding this process poses challenges for traditional research methods as segregation dynamics exhibit many complex behaviours including interdependency, emergence and nonlinearity. In recent years, simulation has been turned to as one possible method of analysis. Despite this increased interest in simulation as a tool for segregation research, there have been few attempts to operationalise a geospatial model, using empirical data for a real urban area. This thesis contributes to research on the simulation of social phenomena by developing a geospatial agent-based model (ABM) of residential segregation from empirical population data for the Helsinki Metropolitan Area (HMA). The urban structure, population composition, density and socio-spatial distribution of the HMA is represented within the modelling environment. Whilst the operational parameters of the model remain highly simplified in order to make processes more transparent, it permits exploration of possible system behaviour by placing it in a manipulative form. Specifically, this study uses simulation to test whether individual preferences, based on social homophily, are capable of producing segregation in a theoretical system which is absent of discrimination and other factors which may constrain residential choice. Three different scenarios were conducted, corresponding to different preference structures and demands for co-group neighbours. Each scenario was simulated for three different potential sorting variables derived from the literature; socio-economic status (income), cultural capital (education level) and language groups (mother tongue). Segregation increases in all of the simulations, however there are considerable behavioural differences between the different scenarios and grouping variables. The results broadly support the idea that individual residential choices by households are capable of producing and maintaining segregation under the right theoretical conditions. As a relatively novel approach to segregation research, the components, processes, and parameters of the developed model are described in detail for transparency. Limitations of such an approach are addressed at length, and attention is given to methods of measuring and reporting on the evolution and results of the simulations. The potential and limitations of using simulation in segregation research is highlighted through this work

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Cultural Norms of Clinical Simulation in Undergraduate Nursing Education

Simulated practice of clinical skills has occurred in skills laboratories for generations, and there is strong evidence to support high-fidelity clinical simulation as an effective tool for learning performance-based skills. What are less known are the processes within clinical simulation environments that facilitate the learning of socially bound and integrated components of nursing practice. Our purpose in this study was to ethnographically describe the situated learning within a simulation laboratory for baccalaureate nursing students within the western United States. We gathered and analyzed data from observations of simulation sessions as well as interviews with students and faculty to produce a rich contextualization of the relationships, beliefs, practices, environmental factors, and theoretical underpinnings encoded in cultural norms of the students’ situated practice within simulation. Our findings add to the evidence linking learning in simulation to the development of broad practice-based skills and clinical reasoning for undergraduate nursing students

Opinion Dynamics and Communication Networks

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as $k$--dimensional bit--strings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold $d_I$. Depending on $d_I$, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters $d_I$ and $k$, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non--trivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real--world communication patterns.Comment: 14 pages 6 figure

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training