Thank you communication network in organization 感謝ネットワークからみる組織のコミュニケーションの形Hiroko Onari
感謝ネットワークからみる組織のコミュニケーションの形Thank you communication network in organization. Engaged employees tend to say "thank you" with the reason of the appreciation. The managers who have an excellent vocabulary motivate and inspire their subordinates.
Impact of weak internal information in the team.How to build great the team. How to unite the team.Key factor is transactive memory and strong network of organization. Strong network increases the bandwidth of communication channel.Hidden profiles, anchoring and adjustment heuristic.
Thank you communication network in organization 感謝ネットワークからみる組織のコミュニケーションの形Hiroko Onari
感謝ネットワークからみる組織のコミュニケーションの形Thank you communication network in organization. Engaged employees tend to say "thank you" with the reason of the appreciation. The managers who have an excellent vocabulary motivate and inspire their subordinates.
Impact of weak internal information in the team.How to build great the team. How to unite the team.Key factor is transactive memory and strong network of organization. Strong network increases the bandwidth of communication channel.Hidden profiles, anchoring and adjustment heuristic.
Dois milhões de trabalhadores morrem a cada ano de doenças ocupacionais e acidentes no trabalho. A agricultura responde por mais de 50% das mortes, ferimentos e doenças no trabalho. O relatório da OIT mostra que a principal causa de morte por problemas ocupacionais é o câncer, seguido por doenças circulatórias e acidentes.
The document contains contact information for Guangxi Hongfa Heavy Machinery Co.,Ltd located in Yiling Yan Industrial Zone, Nanning, Guangxi, China. It lists the company's address, mobile number, telephone number and email contact (ivy@hfbrickmachine.com) repeated numerous times.
This document discusses statistical modeling of behavioral big data. It explains that with observational data, the relationship between cause and effect is difficult to determine from simple comparisons. Statistical modeling involves modeling the data generation process and fitting the model to the data. This allows controlling for other factors that influence both the outcome variable and explanatory variables. The document provides examples of probability distributions commonly used in statistical modeling and discusses issues that arise when the outcome variable is heavy-tailed. It proposes some approaches to dealing with heavy-tailed outcome variables, like using an intervening variable or modeling the increment of the outcome variable.
How Do Newcomers Blend into a Group?: Study on a Social Network GameMasanori Takano
The document describes a study analyzing data from a social network game to understand how newcomers blend into groups. Key findings include:- Newcomers tended to cooperate with existing group members without receiving prior cooperation, showing they cooperate to construct new relationships. - Newcomers were more influenced by messaging from others compared to existing members, indicating messaging helps newcomers integrate. - Both newcomers and existing members were more likely to cooperate with and respond to the cooperation of newcomers over existing members, demonstrating groups accept newcomers.The analysis provides insights into how humans use reciprocal altruism to construct cooperative relationships with strangers and integrate newcomers into groups.
新参者は如何にして新たなグループになじむのか? ソーシャルゲームにおける分析事例 | WEBDB Forum 2015Masanori Takano
- CyberAgent's Akihabara Lab analyzes large-scale data from social games and services to improve user experience and service quality. - The lab studied how newcomers integrate into social groups in a social network game. Regression models analyzed newcomers' cooperative behavior and how much cooperation they received. - Results showed that newcomers often cooperated with group members without needing reciprocation first, and were more likely to receive cooperation than existing members, suggesting humans readily accept strangers into cooperative relationships.
1) The document is a profile for TokyoWebmining, which is part of the Ameba Technology Laboratory at CyberAgent, Inc. It provides information about their research interests and activities. 2) Their research focuses on web mining, business intelligence, and developing new technologies like recommender systems and UI design using JavaScript.3) One of the researchers, Kenji Matsuda, tweets at @mtknnktm and their work involves analyzing human cooperation using concepts like kin selection, direct and indirect reciprocity, and multilevel selection.
1.社会関係の数と親密さのトレードオフが社会構造に与える影響2017 Feb. 26, 27 – 第⼀回 計算社会科学ワークショップCyberAgent, Inc. All Rights Reserved株式会社サイバーエージェント技術本部 秋葉原ラボ◯⾼野雅典, 福⽥⼀郎1Masanori Takano and Ichiro Fukuda."Limitations of Time Resources in Human Relationships Determine Social Structures",Palgrave Communications, 2017 (in press).
3.知りたいことヒトの社会的グルーミングの進化• ⾮ヒト霊⻑類: ⽑づくろい → ヒト: 会話、⽬配せ- Kobayashi H and Kohshima S (1997) Unique Morphology of the Human Eye. Nature 387(6635): pp 767–768.- Dunbar RIM (2004) Gossip in Evolutionary Perspective. Review of General Psychology 8(2): 100–110. 251現代⼈の多様な社会的グルーミングの使い分け• 知⼈・友⼈: SNSなど → 親密な関係: Face to Face・電話- Burke M and Kraut RE, Growing closer on facebook: changes in tie strength through social network site use. In: CHI, pp 4187–4196 (2014).3
4.タイトル TITLEヒトの社会関係は偏っている• べき分布: ランダムではこのような偏りは⽣まれない• 少数の親密な友⼈と、多数の顔⾒知り• 様々なデータでべき分布が存在携帯電話、E-mail、Facebook、Twitterなどref:- W. Zhou, D. Sornette, R. Hill, R. Dunbar, "Discrete hierarchical organization of social group sizes", Proceedings of the Royal Society B: Biological Sciences, 2005- Chaoming Song, Dashun Wang, and Albert-László Barabási, Connections between Human Dynamics and Network Science, arxiv.- V. Arnaboldi, M. Conti, A. Passarella, F. Pezzoni, "Analysis of Ego Network Structure in Online Social Networks", PASSAT, 2012.4ヒトの社会関係の偏り社会関係イメージ親密さ社会関係の強さ(頻度)の分布
5.タイトル TITLE• 社会的グルーミングは時間的なコストが⼤きい・ヒトはおおよそ⽇中20%を充てているDunbar, R. I. M. (1998). Theory of mind and the evolution of language. In: Approaches to the Evolution of Language:250 Social and Cognitive Bases, Cambridge University Press: Cambridge, pp 92–110.• 社会関係のべき分布はYule-Simon過程で⽣成Pachur, T., Schooler, L. J. and Stevens, J. R. (2012). When Will We Meet Again? Regularities of Social Connectivity and222 Their Reflections in Memory and Decision Making. In: Simple Heuristics in a Social World, Oxford University Press:223 Oxford, pp 199–224. Yule-Simon過程(the rich get richer) ・社会関係強化の確率 ∝ 社会関係の強さ5なぜこのような偏りを⽰すのか?
6.タイトル TITLE• 社会的グルーミングは時間的なコストが⼤きい・ヒトはおおよそ⽇中20%を充てているDunbar, R. I. M. (1998). Theory of mind and the evolution of language. In: Approaches to the Evolution of Language:Social and Cognitive Bases, Cambridge University Press: Cambridge, pp 92–110.• 社会関係のべき分布はYule-Simon過程で⽣成Pachur, T., Schooler, L. J. and Stevens, J. R. (2012). When Will We Meet Again? Regularities of Social Connectivity andTheir Reflections in Memory and Decision Making. In: Simple Heuristics in a Social World, Oxford University Press:Oxford, pp 199–224. ・社会関係強化の確率 ∝ 社会関係の強さ 社会的グルーミングの戦略は ヒトは社会関係の強さに依存6社会的グルーミング戦略
14.Individual-based Simulationによる仮説検証モデル概要• 各個体は資源 R を持ち、各ステップ(⽇)に R を消費して社会関係の新規構築 or 強化をする• 強化はYule-Simon過程に従う(社会関係の強さに⽐例して強化する)※ 社会関係の強さに⽐例して R の消費量が決まる(コスト関数) → つまり強い社会関係を強化すると、強化できる社会関係の数を 少なくなるこのコスト関数 α dij + β のパラメータ(αとβ)を調整してデータにフィットさせる14IndividualsSocial Grooming
16.親密さ d に基づくコスト増の勾配 α の影響勾配αによって社会構造が変化160501001502000 20 40 60dvコスト勾配関数(傾きα)傾き α が緩やかになるほど、狭く深い社会構造に
17.まとめ社会関係の数(N)と社会関係の強さ(m)にトレードオフ• C=Nma (a > 1) に従う• a > 1は親密であるほどコミュニケーションコストが増加することに起因コミュニケーションコスト増加の勾配が社会構造に影響• 勾配がきつい: 薄く広い社会構造(Twitterとか?)→ 初対⾯コストが低く、維持コストが⾼い。• 勾配がゆるい: 深く狭い社会構造(対⾯や電話など?)→ 初対⾯コストは⾼いが維持コストは低い。17
18.コミュニケーションシステムの変化・使い分けヒトの社会的グルーミングはライトな⽅向に進化 (⽬配せ・うわさ話など)- Kobayashi H and Kohshima S (1997) Unique Morphology of the Human Eye. Nature 387(6635): pp 767–768.- Dunbar RIM (2004) Gossip in Evolutionary Perspective. Review of General Psychology 8(2): 100–110. 251→ 社会構造は薄く広く→ ⼤きな社会集団が維持可能になったことを⽰唆知⼈とはFacebook、親密な間柄は電話・直接- Burke M and Kraut RE, Growing closer on facebook: changes in tie strength through social network site use. In: CHI, pp 4187–4196 (2014).→ 維持コストの切⽚・勾配の違いが使い分け要因の可能性18Twitter携帯電話仲の良さ維持コスト
20.20Masanori Takano and Ichiro Fukuda."Limitations of Time Resources in Human Relationships Determine Social Structures",Palgrave Communications, 2017 (in press).http://arxiv.org/abs/1605.07305