基于复杂网络的知识传播知识管理与组织传播视角的模拟分析
https://www.wendangku.net/doc/035286404.html,
Knowledge Communication based on Complex Networks:1
A Simulation Analysis of Knowledge Management and
Organizational Communication
Jianxun Chu*, Shukun Tang, Xiaowei Fang, Guan Wang, Guo Ma
Department of Sci-Tech Policy and Communication,
University of Science and Technology of China(USTC)
Hefei, PRC 230026
*corresponding author email: chujx@https://www.wendangku.net/doc/035286404.html,
Abstract
This paper focuses on the simulation of knowledge communication (KC) with the
ideas of epidemic theory and complexity theory. According to SIR and SIS models,
we build the KC dynamic model on simulating the mechanism of KC as complex
adaptive systems. From interdisciplinary background, we present several necessary
theoretical assumptions and then focus on two main hypotheses: 1) the speed of KC
inspires the emergence of complex networks; 2) the structure of KC network
influences its efficiency. The paper, in a macro-perspective, simulates the universal
model with probability coefficients in different situations, analyzes the results of the
curves comparing the small-world network and the random network in order to
explain the structural difference, and then discusses the future research combining this
simulation and empirical sampling statistics.
Keywords:knowledge communication; complex networks; simulation; organizations
1This paper is supported by the NSFC (No.70503024), the Fund of CAS President Prize, and the Fund of Graduate Innovation from USTC.
1Introduction
In some fields of the organizational science research, many scholars have already focused on organizational learning and knowledge communication behaviors for dozens of years, and most of the research achievements explained particularly human organizational behaviors and communication models in some certain organizations. (Argyris & Sch?n, 1978; Nonaka & Takeuchi, 1995; Davenport & Prusack, 2000; Corman, Kuhn, Mcphee, & Dooley, 2002; Malone, 2002; Monge & Contractor, 2003; Fulk, Heino, Flanagin, Monge, & Bar, 2004). Exploring the social network as well as organizational communication has emerged as an important sight within the studies of human organizations. Networks have become prominent as a theoretical framework for explaining personal behaviors between and in organizations with the background of the inter-disciplinary science (Miller and Monge, 1985; Uzzi 1996; Cowan, Jonard, & Ozman, 2004; Yuan, Fulk, Shumate, Monge, Bryant, & Matsaganis, 2005). Network researchers have sought to explain organizational phenomena in terms not only of formal organizational structures, but also of informal organizational structures, such as communication networks, knowledge networks, influence networks, advice networks, task networks and the networks of innovation diffusion(Rogers, 1995), which can be integrated into the emergent communication networks (Monge & Eisenberg, 1987; Stohl, 2001).
In a micro-perspective, using the concepts and methods of the social network analysis (Scott, 2000; Wasserman & Faust, 1994), Contractor and his colleagues developed a simulation software BLANCHE for computing the attitudes of Multi-Agents in different organizations, and later they wanted to discover the factors to make the evolution of the communication network (Carley, 1995, 2002; Contractor, Fonti, Steglich, Su, & Whitbred, 2004). In their book, Monge and Contractor (2003) summarized many theories in social science to explain the reasons on the motivation and communication behavior of every agent in or between organizations. The most important method, which they prefer, is to combine more and more theories from social science, especially for psychology, sociology, economics, and organizational communication, into a strong modeling approach named “MTML” (the multi-theory, multilevel approach), which is a useful and smart way to deal with the academic theoretical analysis and application. However, it’s not enough for the further research on organizational communication following those ideas, though they regarded the communication and knowledge networks as complex systems, and mentioned small world networks (Watts, 1999, 2003; Watts, Dodds, & Newman, 2002) to deepen the research on topologic structural of communication networks and their dynamic mechanism on complex adaptive systems (Holland, 1995).
This paper, firstly, follows the above ideas and some social theories selected by Monge and Contractor (2003), and secondly, we should pay more attentions to the topologic structure of communication networks in a macro-perspective. Hence, in this paper, we want to absorb some new thoughts from the complexity science (Gell-Mann, 1994; Holland, 1995, 1998; Anderson, 1999), and borrow some novel theoretical paradigms and comprehensive models of the complex networks, such as small-world networks and scale-free networks (Watts & Strogatz, 1998; Albert, Jeong, & Barabasi, 1999; Comellas, Ozón, & Peters, 2000), and hope to simulate the dynamic mechanism of communication networks in a new sight of the
theoretical framework. What’s more, this paper describes a new method to build the knowledge communication models by analogy with epidemic theory, such as SIR and SIS models (Kuperman & Abramson, 2001; Saramaki & Kaski, 2005). It is described in further detail in the later section, and now we can build the theoretical models and show the results from the simulation by computer before we have enough statistic data. Additional, we hope to enlighten more scholars in this kind of inter-disciplinary research to broaden more fields of theoretical simulation analysis on communication theories.
2Methods
2.1Theories of Complexity
Complexity has become a prominent theoretical framework and analytic method in many fields of both natural and social science for decades. In organization science, we can class the complexity as two kinds of phenomena that one is based on individual interaction, and the other is based on structural diversity (Tang & Chu, 2001). The complexity from the individual interaction (Simon, 1996), such as complex adaptive system (CAS) by John Holland (1995, 1998) and the multi-agent system (MAS) in simulations, describes that the unpredicted, chaotic complex systems come from several simple rules between agents in an organization by a micro-level perspective (Gell-Mann, 1994). However, the complexity from the structural diversity, such as the topologic structure of the complex networks, presents the emergence and evolution of the whole organization by a macro-level perspective (Kauffman, 1993; Newman & Girvan, 2004).
2.2 Theories of Complex Adaptive System
Having acquired images of iris, the next step is to isolate the iris from the rest of the image, namely, finding both the inner (pupillary) boundary and the outer (sclera) boundary.
In human organizations, human beings can apperceive environments and make decisions according to their own judgement, so a well-rounded society must be a sort of complex adaptive system, just like what Holland described in his book (1995). CAS gives us a new concept and method to analyze the complex problems in human organization, such as organizational behaviors, organizational communication, and knowledge communication. In the history of system theory, the idea of General Systems Theory was back to biologist Ludwig von Bertalanffy (1968) and the concept of dynamic systems was born to offer innovative ways of understanding the relationship among functioning components(Forrester, 1961; Senge,1990). Later, there were some ideas of Self Organized Criticality (SOC, Tang & Bak, 1988) and
self-organizing complex systems (Contractor & Seibold, 1993). Here, we regard individuals as smart agents with some certain interactions with others in our research, and their decisions are impacted by their neighbors’ attitude. To simplify the CAS, as the beginning of a novel paradigm research, this paper lay on abstract actions, including to communicate and not to communicate.
Assumption One: The interaction between agents will influence their actions and the dynamics of the complex adaptive system.
2.3 Theories of Complex Networks
Recently, studies on complex networks were popular in many fields all over the world, including social networks, communication networks, knowledge networks and epidemic networks. The earliest sociological experiment by a Harvard sociologist Stanley Milgram (1967) proved that there appeared to be a universal rule as “six degrees of separation” between any two individuals. It was not built the serious mathematic model, until in 1998 the model of small-world network was published in Nature (Watts & Strogatz, 1998). Further, in order to study the dynamics of network, the model of scale-free network was built, and later, many empirical data proved it as a universal law for lots of networks, including WWW, Internet, knowledge network and other communication networks (Albert, Jeong, & Barabasi, 1999; Newman & Girvan, 2004).
Therefore, we can use the complex network as an assumption of the general structure of communication network in organizations, and we can compare the regular hierarchical relation organization with complex network organization to find which has the better efficiency in organizational communication.
Assumption Two: Knowledge Communication, supported by enough individuals forming a communication network, shows the general rules of complex networks.
Hypothesis One: The different structure of the complex network will influence the function of knowledge communication.
Fig. 1. The WS model of the small-world network (Watts & Strogatz, 1998)
For example, in the WS model of the small-world network, p from 0 to 1 means from the regular network to random network, while Watts and Strogatz (1998) gave a clever method to rewrite few links to make the effect of small worlds. We can use the ideas to build the small-world network in organizational communication as the above.
2.4 Theories of Organizational Communication and Communication Networks
Organizational Communication
Organizational communication can be divided into many schools with different views, such as classical management, culture, interpersonal relationships, and systems perspective (Putnam & Pacanowsky, 1983; Miller, 2003; Eisenberg & Jr. Goodall, 2004). In the systems perspective, Richard Farace, Peter Monge, and Hamish Russell devoted an organizational communication textbook to the systems approach, thereby endorsing the now commonly heard statement: “Communication is the process of organizing” (Farace, Monge, & Russell, 1977). In recent 20 years, several scholars introduced the paradigms of social network analysis into the traditional research on organizational communication, and built some models to simulate the logical and virtual situations, to compare with the data from the real worlds (Wasserman and Faust, 1994; Carley, 1995, 2002; Monge & Contractor, 2003).
This paper, as the first phrase research, wants to explain the theoretical models and simulation results, and on the next phrase of the future research, we will test the theoretical models with the empirical data from samples.
Knowledge Communication and Diffusion of Innovations
In human organizations, it is important to communicate knowledge with other members, and many scholars focus on the knowledge communication (Dierkes, Berthoin Antal, Child, & Nonaka, 2002; Monge & Contractor, 2003) and the diffusion of innovation (Rogers, 1995; Zhu, Weaver, Lo, Chen, & Wu, 1997).
Argyris and Sch?n (1978) early mentioned the concepts of organizational learning and knowledge sharing, and then many scholars promoted the studies on knowledge management and knowledge communication (Davenport & Prusack, 2000). But, most of these researches formerly based individual’s behaviors and management methods in a micro-lever, while this paper will refer more from the famous Rogers’ Diffusion of Innovations Theory in a macro-lever, which can give us more ideas of exploring knowledge communication in human organization networks.
Rogers (1995) stated that the cumulative number of adopters typical follows an S-shaped curve. Generally, early adopters appear to weigh their personal needs more, and they are more actively searching for information, whereas late adopters appear to attach more weight to their social needs, have a lower aspiration level and search less for information. The speed and degree to which an innovation diffuses is
related to several factors, including the communication channels involved, the nature of the social networks in which the innovation is placed, and the extent of the change agents’ promotional efforts affect the rate of adoption (Janssen & Jager, 2002).
However, when the communication network play an important role, or when people are uncertain about what to do during the unstable process, their attitudes on some new knowledge will be easily changed by the neighbors in the networks. So, it is necessary to divide the process to accept some certain knowledge into three steps: 1) to touch knowledge, 2) to understand knowledge, and 3) to accept knowledge. This paper focuses on the individual behavior and the structural dynamics of knowledge communication in communication networks.
Assumption Three: Knowledge communication process is the mental process through which an individual passes from 1) to touch knowledge, 2) to understand knowledge and 3) to accept knowledge.
Theories of Social Science and MTML simulations
Our research on organizational communication as well as knowledge and crisis communication partly depended on Monge and Contractor’s book named “Theories of Communication Networks” (Monge and Contractor, 2003). In their book, they built the wonderful theoretical approach: the multi-theory, multi-level approach (MTML), which is a useful tool to simulate the individual’s communication behaviors in a mirco-perspective. They selected many social theories integrated together, simulated the virtual situation, and adjusted the parameters to make the model better. It seemed to be able to solve all kinds of problems in organizational communication, but we should discuss more problems in details. For examples, how many theories we should select from the infinite set of social theories? What is the better formulation to combine those theories from all different angles of view? And how to build the logical deduction between individual’s micro-perspective and structural macro-perspective?
In a word, since it is difficult to give the answers, this paper just presents a view of epidemic models to test the changing mechanism of an organizational communication network, for example knowledge communication, with the concepts and ideas of the theories of communication networks (Palazzolo, Serb, She, Su, & Contractor, 2002).
2.5 Epidemic Theory and Simulation Models
Just as what Newman mentioned (2003), one of the original, and still primary, reasons for studying networks is to understand the mechanisms by which diseases and other things (information, computer viruses, rumors) spread over them. It is available for exploring the communication network in human organizations. There are two kinds of basic models, such as the SIR Model and the SIS Model of epidemic theory.
The SIR model (Hethcote, 2000; Newman, 2003) divides the population into three classes: i) susceptible (S), meaning they don’t have the disease of interest but can catch it if exposed to someone who does; ii) infective (I) meaning they have the disease and can pass it on; and iii) recovered (R) meaning they have recovered from the disease and have permanent immunity, so that they can never get it again or pass it on. Some authors consider the R to stand for “removed” or “refractory”, both meaning the same sense. In traditional mathematical epidemiology, one then assumes that any susceptible individual has a uniform probability β per unit time of catching the disease from any infective one, and that infective individuals
recover and become immune at some stochastically constant rate γ. The fraction ,, and s i r of individuals in the states S, I, and R are governed by the differential equations: is dt
ds β?=, i is dt di γβ?=, i dt dr γ=. (1) This type of model is the simplest, and although they have taught us much about the basic dynamics of diseased, they are obviously unrealistic in their assumptions.
However, not all diseases confer immunity on their survivors. Some individuals will be cured and can perhaps obtain the diseases again soon, which is just like the knowledge communication. When some individuals gain one certain knowledge, which is not meaning they need not learn new knowledge any more, they perhaps be infected again with the fresh knowledge just like the fresh diseases. The theoretical mechanism is the similar between the spread of diseases and knowledge via changing some parameters in the models. Another model from epidemic theory, the SIS Model, will be useful in our research analysis. The SIS model (Pastor-Satorras & Vestpignani, 2001; Newman, 2003), in the simplest, fully mixed, single-population case, can be described by the following differential equations :
i s i dt ds γβ+?=, i s i dt di γβ?= , (2)
where β and γ are, as before, the infection and recovery rates.
The SIS model cannot be solved exactly on a network as the SIR model can, but a detailed treatment has been given by Pastor-Satorras and Vestpignani (2001) for SIS epidemics on the configuration model.
However, this paper will not touch those physical and mathematical reasoning, but some useful concept and results to explain the knowledge communication and the dynamics of communication networks. Hence, we suggest that we should use the epidemic theoretical models and simulation methods to test the spread of knowledge communication in complex network organizations.
Assumption Four: Epidemic Theory is the abstract theoretical models of networks, and it is reasonable to compare the models of epidemic theory with the spread of knowledge communication in human organizations.
Hypothesis Two: The speed of spread, deeply influenced by time t, will inspire the emergence mechanism of the knowledge communication in complex networks.
3Methods
3.1 Ideas of Modeling and Concepts
The ideas of modeling Knowledge Communication based on the above theories. First, we borrow the ideas and concepts form epidemic theory, including SIR model and SIS model (Newman, 2003), because the spread of knowledge in an organizational network, just as epidemic theory, presents several kinds of characters: Susceptible (S), Infective (I), and Recovered (R).
Here, as for knowledge communication, the susceptible individual means one who has no this kind of knowledge but is ready to learn; and the infective means one who has learnt the knowledge and is infected by new things, just like disease; and the recovered means one who gains the knowledge, but he or she does not like to accept the knowledge any more, just like immunity (Hethcote, 2000).
Additional, another mechanism of knowledge communication is that one can receive the knowledge for several times and accumulate the understanding of the knowledge more and more until acceptation. However, at the same time, there is a chance to decrease the knowledge when the agent is changed by its neighbors who have no contribution until to be the susceptible individual again. Therefore, this model can describe the dynamical mechanism of knowledge communication from first touching the knowledge, to forming attitude towards the knowledge, and to making decision to adopt or reject the meanings of knowledge (Holland, 1995; Rogers, 1995).
This paper will absorb the ideas and concepts from SIR model and SIS model, and combine them into a new model as following. According to our assumption one and three, we want to classify the individuals in the network of knowledge communication as following A, B, C, and D: A stands for the state of whose who have no knowledge, while B, C, and D show the different degrees and attitude of having knowledge.
In details, B stands for the individual who has chance to touch the knowledge, but has no ideas to transfer, just like the delitescence of a disease; C stands for the individual who understands the knowledge but is perhaps to change the attitude; D stands for someone who learns to accept the knowledge and is ready to communicate with his or her neighbors.
3.2 Methods of Modeling and Simulation
In our model, we define the value of the different states as N(A)=0, N(B)=1, N(C)=2, N(D)=3 in the knowledge communication networks.
If Ni = 0, then agent i has no knowledge, but when i is surrounded by several agents j with Nj = 2 or 3, the agent i perhaps learns more knowledge from his neighbors j. However, if Nk = 3, the agent k has strong background of that knowledge, so the rule is that k will influence his neighbors to make them from 0 to 1, or from 1 to 2, or from 2 to 3. In this model, the dynamics of knowledge communication bases on the rules of the knowledge exchanging between one-one neighbors.
Given Ni = x, ( x = 0, 1, 2, 3), if agent i has n neighbors marked as the nearby agent ij (j=1,2,3,…, n), then: The value of knowledge exchanging: n N N
n j ij i ∑
==?1
. (3)
New Value of agent i :
(4) t
i t i t i N N N ?+=+1Special rules as following: If , then . (5)
3max 1=≥+N N
t i 31≡+t i N Approximation Rules: []33,5.211≡?∈++t i t i N N , [)25.2,5.111≡?∈++t i
t i N N , [)15.1,5.011≡?∈++t i t i N N , [)05.0,011≡?∈++t i t i N N (6)
Just as what we discussed, the above rules are reasonable with the test of our later results of simulation the models. Here is a hidden mechanism to make the knowledge outdated or decreased by the dynamics of networks. For example, Ni=0, and its neighbors are Nij=0, 1, 2, 2, 3. So, ?Ni= (0+1+2+2+3)/5=1.6 ≡2. If Ni=0, 0, 0, 1,1, then ?Ni= (0+0+0+1+1)/5=0.4 ≡0, which means the agent i gets very few knowledge from its neighbors.
However, in this model, the N is always a non-decreasing function, so there is another mechanism to judge how to decrease one’s value of knowledge. In order to improve the function (3) and (4), we can introduce the probability of states changing pabcd, and give the weight of influence wij . Therefore, the more complex model can be wrote as following formulation:
)1,1,(,,,,1,,,11==≡??=∑∑∑∑≠=≠=+d c b a n j ij i ii d c b a n j ij ij t i
p w N N p N w N ?φφ??φφ? (7)
Fig. 2 Agent i’s neighbors in Knowledge Communication Network
Here, according to S-curve by Rogers (1995), after testing lots of former empirical data, we can concluded there are some relationship between the above parameters. For example, p01 (the probability from 0 to 1) < p12
The following methods of simulation in this paper, we compare the random network with the small-world network as human communication network in details. What’s more, we can adjust the parameters to fit the real world and discuss deeply on the topics. If we can control some nodes with high degree in complex network, for example, the number of n meaning its connection degree with others, we can design more efficient mechanism and organizations for knowledge communication in the future.
Fig. 3. The Method and Process of Simulating Knowledge Communication on Networks
4Results and Analysis
The following graphs are the results of simulation based on the model of small-world network (SWN), and we can analyze the statistic data of the population with the states of D, C, and B, which reflects the efficient of the knowledge communication (KC) in human organizations.
Fig. 4(a) KC efficient on SWN (20, 0.1, 0.05)
This is the small-world network with average
degree < d > =20, the probability of rewriting is
p=0.1, the probability of N=3 is q=0.05.
Fig. 4(b) KC efficient on SWN (10,0.1, 0.05)
This is the small-world network with average
degree < d > =10, the probability of rewriting is
p=0.1, the probability of N=3 is q=0.05.
From the above graphs, we can find that the general trends of the curves are both similar with S-curve by Rogers (1995), which proves the correction of Rogers’ theory from another view. Comparing the different average degree of the two networks, it seems that Fig. 4(b) is faster that Fig. 4(a), for example, in Fig. 4(a) t=6, N(B)=200, but in Fig.4(b), t=6, N(B)=800.
We repeated many times to simulate the dynamics of evolution, and obtained the similar results. It’s a bit surprised us that we had thought the more of
The following graphs present us the results to compare the small-world network (SWN) with the random
network (RN) as different structural feature in a macro-perspective. We hope to test the above hypotheses via simulations to give a general trend curve.
The comparative results describe as follow:
ig. 5(a) KC efficient on SWN (30, 0.1, 0.05)
rage
Fig. 5(b) KC efficient on RN (30, 0.1, 0.05) gree < e chose the same parameters of the network except for their structure, and the results show that the random ionally, B curve in random network shows a surprising drop when t=
5 (the critical point of phase
F This is the small-world network with ave degree < d > =30, the probability of rewriting is
p=0.1, the probability of N=3 is q=0.05
This is the random network with average de d > =30, the probability of N=3 is q=0.05 W network has lower efficiency than the small-world network in the first steps, while later the random network improves so fast once the curve passes the threshold and leads a phase transfer. In Fig. 5(a), the increase of the curve is even, while the change of the curve in Fig. 5(b) is sudden and unpredictable. Generally, the D’s trend of curve in random network has a critical point: t=5, and after that point the value increases from 200 to 900 while t is from 5 to 6. At the same situation, the increase of curve in small-world network is from 300 to 350.
Addit transfer). The reasonable explanation is that after t=5 the random network’s spread makes the average short path appeared. We can use this feature to improve knowledge communication in random organizations.
5 Conclusion and Discussion
The study of knowledge communication focuses on borrowing the ideas of the epidemic theory and using the simulation methods to analyze the dynamics mechanism of organizational communication based on complex networks. Scholars have argued that many basic models on SIR and SIS models, and compared the small-world network with the random network based on complexity theories, and also some new paradigms on the knowledge communication networks, such as the MTML approach and methods of simulation. However, in this paper, we want to absorb different backgrounds to explore two main hypotheses: 1) the speed of knowledge communication inspires emergence of networks, 2) the structure of the complex network influences the function of the organization. From the results just mentioned, we can discuss the topic further as following:
First, this paper describes an interesting model to simulate the spread of network, not only disease, but also knowledge. Some probabilistic factors are concerned to the model to make it more general and universal, so we can repeat simulating and sampling the statistic data to fix on the parameters in real worlds. I bet the model will be more and more useful and reasonable.
Second, the results and analysis just mentioned, have brought us many interesting problems in the future research. For example, the formulation (7) in the model has a parameter , comparing with Rogers’ S-curve and three steps to accept knowledge. Perhaps in future research, many empirical sampling data can prove the real dynamic mechanism of the knowledge communication in organizational communication as complex networks.
φ?p This is the beginning research on our project and there are some limitations in the model. The most important problem that we should add more empirical data to test and support our simulation results than before. Here is a wonderful new way to research traditional problems in organizational communication.
Acknowledgement
We will thank Prof. BinHong Wang from USTC(University of Science and Technology of China), Prof. Jonathan Zhu from the CityU of Hong Kong, and Prof. Noshir Contractor from UIUC (University of Illinois at Urbana-Champaign) to push our research.
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基于复杂网络的知识传播2
——知识管理与组织传播视角的模拟分析
褚建勋1，汤书昆，方小伟，王冠，马果
1中国科学技术大学 科技传播与科技政策系(合肥，230026)
通讯作者1 email地址: chujx@https://www.wendangku.net/doc/035286404.html,
摘要：本文主要借鉴复杂性科学与疾病传播模型（例如SIR,SIS等）来研究知识传播扩散模式，建立基于复杂适应系统的知识传播动力模型来模拟其运作机制。我们运用跨学科背景建构其理论前提并验证两个假说：1）知识传播速度影响复杂网络的涌现机制；2）知识传播网络结构影响传播效率。本文从宏观层面调整了不同情境的概率参数进行模拟，比较了小世界网络和随机网络等不同网络结构下的知识传播参数，并讨论了结合模拟分析与实证统计的研究前景。
关键词：知识传播复杂网络模拟组织
通讯作者简介：
褚建勋（1978－），男。中国科技大学管理学院，科技传播与科技政策系，传媒管理方向博士生。
学术兼职：中国系统工程学会系统动力学专业委员会委员，ICA（国际传播学会）国际委员会委员。
研究兴趣：危机传播与传媒管理、网络传播与复杂性科学。
通讯地址：安徽合肥金寨路96号中国科技大学科技传播与科技政策系（230026）。
联系电话：0551-*******（O）. Email: chujx@https://www.wendangku.net/doc/035286404.html, . 传真：0551-*******.
2本课题获国家自然科学基金青年基金（项目编号：70503024），中国科学院院长奖科研启动专项基金和中国科技大学研究生创新基金的课题资助。
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