اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدSocial Learning in a Human Society: An Experimental Study
107
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper presents an experimental study to investigate the learning and decision making behavior of individuals in a human society. Social learning is used as the mathematical basis for modelling interaction of individuals that aim to perform a perceptual task interactively. A psychology experiment was conducted on a group of undergraduate students at the University of British Columbia to examine whether the decision (action) of one individual affects the decision of the subsequent individuals. The major experimental observation that stands out here is that the participants of the experiment (agents) were affected by decisions of their partners in a relatively large fraction (60%) of trials. We fit a social learning model that mimics the interactions between participants of the psychology experiment. Misinformation propagation (also known as data incest) within the society under study is further investigated in this paper.
قيم البحث
اقرأ أيضاً
Simple memorizing tasks have been chosen such as a binary code on a matrix. After the establishment of an appropriate protocol, the codified matrices were individually presented to 150 university students who had to memorize them. A computer simulati
on for a similar task is available which uses a perceptron on which an algorithm was implemented allowing for some degree of globality (technically referred to as entropic nonextensivity within a current generalization of the usual, Boltzmann-Gibbs, statistical mechanics). Our main observation is that, for the very specific learning task on which we focus here, humans perform similarly to slightly nonextensive perceptrons.
The small world phenomenon, popularized by Stanley Milgram, suggests that individuals from across a social network are connected via a short path of mutual friends and can leverage their local social information to efficiently traverse that network.
Existing social search experiments are plagued by high rates of attrition, which prohibit comprehensive study of social search. We investigate this by conducting a small world experiment at Burning Man, an event located in the Black Rock Desert of Nevada, USA, known its unique social systems and community participation. We design location-tracking vessels that we routed through Burning Man towards the goal of finding a particular person. Along the way, the vessels logged individual information and GPS data. Two of fifteen vessels made it to their designated people, but a month after Burning Man. Our results suggest possible improvements to limit rates of attrition through community participation and a design methodology that emphasizes cultural practices to aid social experimentation.
We perform laboratory experiments to elucidate the role of historical information in games involving human coordination. Our approach follows prior work studying human network coordination using the task of graph coloring. We first motivate this rese
arch by showing empirical evidence that the resolution of coloring conflicts is dependent upon the recent local history of that conflict. We also conduct two tailored experiments to manipulate the game history that can be used by humans in order to determine (i) whether humans use historical information, and (ii) whether they use it effectively. In the first variant, during the course of each coloring task, the network positions of the subjects were periodically swapped while maintaining the global coloring state of the network. In the second variant, participants completed a series of 2-coloring tasks, some of which were restarts from checkpoints of previous tasks. Thus, the participants restarted the coloring task from a point in the middle of a previous task without knowledge of the history that led to that point. We report on the game dynamics and average completion times for the diverse graph topologies used in the swap and restart experiments.
Here, we review the research we have done on social contagion. We describe the methods we have employed (and the assumptions they have entailed) in order to examine several datasets with complementary strengths and weaknesses, including the Framingha
m Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a three degrees of influence property, and we review statistical approaches we have used to characterize inter-personal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations.
Human trafficking is a serious social problem, and it is challenging mainly because of its difficulty in collecting and organizing related information. With the increasing popularity of social media platforms, it provides us a novel channel to tackle
the problem of human trafficking through detecting and analyzing a large amount of human trafficking related information. Existing supervised learning methods cannot be directly applied to this problem due to the distinct characteristics of the social media data. First, the short, noisy, and unstructured textual information makes traditional learning algorithms less effective in detecting human trafficking related tweets. Second, complex social interactions lead to a high-dimensional feature space and thus present great computational challenges. In the meanwhile, social sciences theories such as homophily have been well established and achieved success in various social media mining applications. Motivated by the sociological findings, in this paper, we propose to investigate whether the Network Structure Information (NSI) could be potentially helpful for the human trafficking problem. In particular, a novel mathematical optimization framework is proposed to integrate the network structure into content modeling. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed framework in detecting human trafficking related information.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
