Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
An Agent-based Approach to Study Virtual Learning Communities Yiwen Zhang, Mohan Tanniru Department of Management Information Systems The University of Arizona yiwen,
[email protected] Abstract This paper focuses on agent-based approach to study the relationship between the individual behavior of participants and the overall development of a virtual community, to help people to better understand the interactive process, forecast and manage the community development. In this paper, we first analyze the strengths of agentbased approach and the features of virtual communities. We argue that agent-based modeling and simulation approach is well suited to study virtual communities. We then build an agent-based model for virtual learning communities (VLCs). Each participant in VLCs is modeled as an agent with cognitive and social characteristics. The interactive activities are decomposed into four modules: registration module, activation module, action module, and adaptation module. A discussion of general observations of the community behaviors and managerial implications is presented based on series of comparative simulations.
1. Introduction
interactions among individuals, and the community influences those interactions at the same time, such as the direction, strength and content of a particular interaction. A system view and a set of appropriate system approaches are needed to study such a dynamic process to help better understand the evolution of communities and forecast and manage such an evolution. System dynamics and agent-based approach are two widely acknowledged system approaches. In the recent literature, researchers started to employ system dynamics approach [2-4] to study virtual communities, while agentbased approach is still rare in the virtual community literature. This paper aims to promote agent-based approach. We first argue that agent-based approach is well suited to study virtual communities. We then build an agent-based model for one important type of virtual communities -- virtual learning communities. In Section 4, we control and vary major parameters in the model, based on which we conduct series of comparative simulations. In the last section we make general observations and suggest managerial implications based on the simulation results.
2. Agent-based approach vs. system dynamics
“Virtual communities” are communities formed through computer-mediated communications (CMC) [1]. There has been a growing interest in virtual communities, and research has been conducted from philosophical, educational, social, technological and economical perspectives. Many interesting questions have been addressed including special behaviors (e.g. lurker) of individual participants, interaction patterns among group members, and the overall performance or life cycle of a community. Among the literature, individual behavior and community development form two ends of a continuum. At one end, the individual participants, who have their own interests and motivations, carry on their own tasks and interact with each other in the virtual community. At the other end, the community evolves over time and is represented by the fluctuating population, accumulated content, and various interaction patterns among participants. A community evolves from the various
A system approach distinguishes itself from the more traditional analytic approach by emphasizing the interactions and connectedness of different components of a given system. In this section, we first review the recent literature on how system dynamics approach is used to study virtual communities. We then compare agent-based approach with system dynamics approach and suggest that agent-based approach could complement system dynamics approach. Further analyses of the features of virtual communities and the recent research progress of both virtual communities and other relevant domains suggest that agent-based approach is well suited to study virtual communities.
2.1. System dynamics and virtual community System dynamics approach is developed in the late 1950's based on system theory. System theory emphasizes the use of feedback loops, both positive and negative, to
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understand behavior. System dynamics models the system as a set of populations and determine the rates of flow of populations, which cause the populations to change over time. System dynamics is considered to provide a perspective, a set of conceptual tools, and a rigorous modeling method [5, 6]. It was only recently that researchers on virtual community have started to take a system view and employed system dynamics approach. For example, Jones et al. [2] viewed the communication in a virtual community as a mass interaction [7], and described a nonlinear feedback loop among the population of participants, the discourse generated by communication, and the communication overload (named as “virtual public user population”, “virtual public discourse” and “virtual public communication overload” in the paper). They built hypothesis of “information cooping” strategy based on this feedback loop and confirmed it by an empirical examination of USENET message. Diker [3] reported research in progress aimed at building a system dynamics model for “open online collaboration communities’. In that model, the author identified a set of feedback loops by integrating social, economic, and technological aspects.
2.2. Agent-base approach vs. system dynamics approach Compared to system dynamics, agent-based approach has a shorter history. It was developed from the literature on “science of complexity” or “complex system” and the research of distributed artificial intelligence in the 1970’s. An agent in a distributed artificial intelligence has the following features: 1) autonomy: goals and mechanisms to pursue the goals; 2) networked interaction: interact with other agents in a networked environment; 3) reactivity: the capability to sense its environment and respond with different behaviors upon the environmental stimuli; 4) and persistency: persistent in their pursuit of goals and operate long time if necessary. These features are also considered the major features of the participants in a complex system. Agent-based approach models the participants as agents and conducts simulation on the model. Recently, researchers pointed out the similarities between the system theory and “complex system” [8, 9]. They argued that “complex scientist” overlooked the literature of system theory and system dynamics, and these two bodies of literature actually target at systems that can be described as nonlinear, complex, and dynamic. They both show success in studying a wide range of systems, such as biological, organizational, and economic systems.
However, system theory and “complex system” do differ in their emphasis. From the comparison made in [8], the major differences are the underlying concept of a system and the unit of analysis. System theory considers the feedback structure as basic building blocks, and system dynamics focuses on the loop structure of both negative and positives feedbacks. On the other hand, “complex system” focuses on studying the emergence of a system, and agent-based approach models the essential characteristics of the individual, simple rules of interaction, and global consequences of the interaction. Table 1 summarizes the major difference between the agent-based approach and system dynamics approach [8]. Table 1. The comparison of agent-based approach vs. system dynamics approach System Dynamics Approach Based on the feedback loop Formal, rigorously quantitative Model continuous events, not model discrete events Used more in confirmatory studies
Agent-Based Approach Based on the simple rules of interaction among individuals Informal Model both continuous and discrete events Used more in exploratory studies
The choice of which method to use depends on the characteristics of both the system and the targeted problems. Researchers suggested a cross-study of agentbased and system dynamics modeling [8], implying that these two methods can complement each other when studying a particular system. A recent study showed [10] that these two methods can both generate rich results and that each performed comparatively better than the other in certain environmental settings.
2.3. Virtual approach
Communities
and
agent-based
We argue that agent-based approach is well suited to study virtual communities. First, each participant in a virtual community can be modeled as an agent in a distributed system. Each participant exhibits features such as autonomy (to join the virtual community), interaction (with other participants), reactivity (to the environment of the virtual community), and persistence (to engage in continuous interaction). Second, reactivity is a critical driving factor in the evolution of virtual communities, and agent-based approach is good at encoding this feature. In virtual communities, participants learn and adapt to the new environment. The strategies of reactivity, which define the rules to cope with other participants or environment, vary among participants and may change over time. Agent-based approach models reactivity at individual level, usually by connecting reactivity to other features of a particular participant. The strategies of a
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
particular agent change over time if his other features change. Third, environmental factors are represented as external factors in agent-based system, which makes the core modeling more focused and the manipulation of environmental setting easy. Agent-based approach usually separates the environmental settings from agent characteristics and their interaction. In a virtual community, managerial policies and technological platform can be modeled as environmental settings. These factors can be simplified when building the core model and modified later without changing the core agent-based model. Current research progress on virtual communities provides adequate support for employing agent-based approach, while it might not be sufficient for system dynamics approach. System dynamics approach requires a thorough understanding of both negative and positives feedbacks. The model in the system dynamics approach usually needs to specify each loop in a quantitative way. However, many individual behaviors and interactions haven’t been thoroughly studied in virtual communities [11]. The recent research of system dynamics approach in virtual communities is either in its initials stage of investigation or is a confirmatory study, without providing a quantitative formulation for each feedback loop in the model. In comparison, agent-based approach only requires an understanding of essential characteristics of the participants and basic interaction rules. Current literature on virtual community has provided sufficient observations and explanations, and can be used by the agent-based approach. Recent research progress in other relevant domains also suggests that using agent-based approach in virtual communities is promising. Agent-based approach has been used in social systems, such as in Computational Organization Theory (COT)[12]. In a recent review of network theory, Monge and Contractor [13] suggested that agent-based approach to the study of complex systems is especially suited to understand knowledge oriented networks. Schrott & Beimborn [4, 14] reported ongoing research using a fixed number of agents to model a closed email network to study the topology and performance of an email network. For these reasons, we use agent-based approach in the rest of paper to model virtual communities and understand their behavior.
3. Building Agent–based model for VLCs The modeling within agent-based approach uses an abstraction of major characteristics of agents and rules of their interaction. The major features of agents and rules vary among the different types of virtual communities,
and we choose Virtual Learning Community (VLC) as the focus of the model. In this Section, we discuss the feature of VLCs and the characteristics of participants and the interaction rules.
3.1. Virtual Learning Community There is no widely agreed categorization of virtual communities, and the definition of VLC also varies. We define VLC as “a group of people who are initiatively engaged in an interactive sharing and learning among themselves through a specific or a set of networking technologies.” This refers to all the virtual communities that are dedicated to the knowledge sharing and learning. Both the Virtual Community of Practice (CoP) and Internet discussion group related to knowledge-intensive topics are examples of VLCs. VLCs play an important role in the current practice of knowledge sharing and learning [15]. In a virtual community, individual participants interact with each other and improve their knowledge and skill by learning, discussion, and sharing. The features of VLCs are suited for an ideal life-long learning environment for knowledge workers envisioned before the wide spread of network [16]. These features are: 1) an open learning environment; 2) finding learning theme based on individual requirement; 3) self-initiated learning compared to passive acceptance of traditional classroom learning; 4) focusing on innovation instead of limiting to the current written material; 5) and life-long learning. While we take a broad view of VLCs, we do exclude learning programs that are well-designed in content and well-guided during processes, including general learning program, web-based and computer-based training (Web/CBT), instructor-led training (ILT), and mentor/apprentice learning environments. More explicitly, a VLC needs to have the following features. 1) Participants have their own learning motivation and initiate a learning process periodically 2) Participants share their knowledge and experience to some extent. Knowledge in the community is contributed by participants and accumulated over time. 3) There is certain amount of interaction among participants, e.g., asking questions and answering questions. 4) Participants make their stay-or-leave decision by themselves without enforcement from others. 5) The community as a whole has a life cycle, including the stages of initiation, development, maturity, sustenance, and dying out.
3.2. Agent-based modeling for VCLs
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We focus on a set of fundamental characteristics and interaction rules among participants and make assumptions of other factors. There are technological factors in a VLC, including factors such as the media richness and human-computer interface design, and managerial factors such as awards program and the involvement of a moderator to monitor the content. Here, we assume that the supporting technology platform is Bulletin Board System (BBS) and there is no synchronous communication. We also assume that all the participants have an equal role in the community, meaning that there are no other roles, such as managers or moderators. These simplifications can be modified in future research. We make three assumptions on the messages posted on a BBS based on general observation. First, the messages are generated from two scenarios and can be categorized into four types. The two scenarios are question-answer and opinion-comment. The four types include asking a question, answering a question, expressing an opinion, and commenting on an opinion. Second, the whole community is developed around one big topic, which includes many subtopics. Each message belongs to one subtopic. Third, in the question-answer scenario, the questions asked have a feature named “difficulty level”, and the answers have a feature named “quality level”. We do not impose “level” to opinion-comment scenario. We refer to “asking a question” or “expressing an opinion” as “an initiation action”, and refer to “answering a question” or “commenting on an opinion” as “response action”. In the following subsections, we present the major characteristics of a participant agent, both cognitive and characteristics, and the interactions among participants. Instead of using specific piece of literature in VLCs, we rely more on classical literature in learning and social interactions for the following reasons. First, the most empirical literature in VLCs is consistent with the ground theory in learning and social interaction. Second, the agent-based modeling prefers a simple abstraction based on most fundamental mechanisms. 3.2.1. Major characteristics of a participant agent A. Cognitive characteristics Interest structure and expertise level Distributed knowledge or expertise has been recognized as the most important factor in collaborative and cooperative learning literature [15, 17]. Vygotsky’s theory of Zone of Proximal Development (ZPD) [18] articulated that in the learning community, where the participants with lower level of expertise can improve their performance with the help from the agents with high level of expertise.
In a VLC, distributed expertise and ZPD also apply. Each agent has interest in a set of subtopics and we use “interest structure” to refer to it. Each agent has his knowledge or expertise on each interested subtopics, and we use “expertise level” to refer to it. Each agent may have different levels of expertise on different subtopics. An agent reads the messages related to his interest, and may reply to the posted opinion, and answer the questions if he has certain level of expertise on that subtopic. Learning characteristics While interest structure and expertise level represent the static status of an agent’s domain knowledge, cognitive ability and cognitive style are the dynamic features of a knowledge acquisition process. They change the interest structure and improve the expertise level. Cognitive ability refers to “the intelligence” on a subject. In a learning community, the agents are considered having comparative “intelligence”, while they have different levels of expertise [15]. Learning styles in the virtual community are dominated by non-traditionalclassroom learning style, such as learning by asking, learning by teaching, and learning by problem solving. The expertise level of an agent increases when it gets satisfactory answers for the questions he asked. Immediate learning incentive Except for the long-term motivation for sharing and learning in a VLC, each agent has a specific purpose for starting a learning session (the duration of one ‘continuous staying’ in the virtual community). The motivation to start a learning session is called “immediate learning incentive”. Usually, there are three situations that initiate a learning session by an agent: having a question to ask (asking a question), having an opinion to share (expressing an opinion), or keeping oneself updated on knowledge shared in the community (periodical browsing). An agent could have multiple incentives to start a learning session. B. Social characteristics As members interact and negotiate, virtual learning communities emerge over time [19]. Researchers use “social capital” to refer to the social aspect involved in the community. Social capital is “a stock of active connections among people: the trust, mutual understanding, and shared values and behaviors that bind people as members of human networks and communities” [20]. Social capital has been used as a framework for understanding a wide range of social issues in temporal communities, such as participation, and cooperation. We will define a set of characteristics that describe the degree to which agents share their knowledge, and the level of activity of their participation.
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Willingness to share and activeness to communicate The extent to which an agent is engaged in participating in a virtual community and the extent to which an agent is willing to share his knowledge or experience varies. We call them “activeness level” and “sharing level” respectively. The activeness level is like “personality” in the real world. For example, some agents respond to others’ postings very frequently. They may reply right away even when the topic is beyond their expertise. Sharing is a virtue in the real world. For example, some agents are willing to share every piece of their knowledge, while some keep it to themselves. The activeness level to communicate and the willingness level to share knowledge together determine how frequently an agent posts messages in the community. The higher the activeness level and the higher the sharing level, the more messages an agent posts. Since these two characteristics lead to the same behavior, we use activeness level to represent both to keep the model simple. Identity and reputation In a virtual community, an agent usually has an identity and reputation. The user name or ID is the identity of an agent in virtual communities. Many VLCs record history of the agents in the community, such as the number of messages posted. The reputation of an agent is based on the quantity and quality of the messages posted by the agent. Some communities use peer-ranking of messages to recognize and label an agent’s reputation. For an agent, the reputation could be a motivation to continuously contribute to the community. If the reputation of an agent is visible to other agents, messages posted by agents with higher reputation may lead to a higher reading rate and higher response rate. We do not model it explicitly in this model. However, it is embedded implicitly in the model if we model activeness level and expertise level, because they are closely related. Loyalty to the community We use “loyalty” to determine whether an agent will leave a community or remain as a member of the community. Literature suggests that both social and intellectual capital are important ingredients for success and add value to organizations and collaborations [20, 21]. We define two constructs for loyalty, intellectual gains and social gains. For an agent, the intellectual gain is related to the increases in the expertise, and the social gain is related to the perceived social support in the community. 3.2.2. Interactions among agents
As we made the assumption that there is no synchronous communication. The agents in a VLC interact with each other through the BBS. Agents read messages from and post messages to the BBS. The environment is represented by the list of messages. Each message explicitly lists the title, author, and time. We know from title which type the message is and what subtopic the message belongs to. The information about the author communicates the difficulty of a question or the quality of an answer. The interaction among agents is captured through learning sessions of agents. The process of a learning session is decomposed into several interdependent and sequential modules or functions -- registration module, activation module, action module, and adaptation module. Figure 1 illustrates the relationships between these modules and how the characteristics of an agent relate to these modules.
Environment (Time = t)
A
Registration Module
Memory from past
Environment (Time = t+1)
D Adaptation Module
Figure 1. The decomposition of a learning session of Cognitive Execution Perception ancharacteristics agent B C Activation Module Immediate learning incentives
Action set
Action Module
Social characteristics
Figure 1. A learning session of an agent A learning session consists of an initialization, four major processes corresponding to four modules, and a loop control. Initialization: the agent looks at the environment (mainly the messages posted) in the VLC. Four major processes: A. Through the registration module, the agent forms a perception based on the environment and his cognitive features (mainly the interest structure). B. Through the activation module, the agent generates an action set from perception, immediate learning incentives, and cognitive and social characteristics as well. C. Through the action module, the action generates results relative to the cognitive characteristics of the agent (mainly the expertise level).
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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005
D. Through the adaptation module, both the cognitive characteristics (mainly the expertise level) and social characteristics (mainly the loyalty to the community) of the agent are adjusted. Loop control: if all the processes have been completed or the time is up for the learning session, the session is completed. Otherwise repeat four major processes. The details of the four modules are described as follows. Note that we use the notation of ‘output = f (input)’. The “f” is used more as a conceptual function than a specific formula. The specific formulations of f are operationalized in the simulation, and are different for different types of agents. A. Registration module
number of current comments is “the conversational inertia”, which is observed from Whittle et al. [7] that a conversion is relatively easy to continue once it started. Qustions to answer = f (questions registered to agent’s interest, quality of the current answers, expertise level, activeness level) Opinions to comment on = f (opinions registered to agent’s interest, activeness level, number of comments a particular opinion has already have) C. Action module The quality of the message is determined by the expertise of the agent. Expression or communication skills are ignored in the module.
At the beginning of the learning session, the agent identifies interested messages using registration module. The messages register into the agent’s perception depends on the number of the new messages from his last visit and his interest structure
Quality of the answers and comments = f (expertise level)
Perception = messages matching the agent’s interest = questions registered to the agent’s interest + opinions registered to the agent’s interest = f (new messages after last visit, interest structure of the agent)
The adaptation module describes how the expertise level improves during the interaction and how the loyalty to the VLC changes. We consider the loyalty to the community consisting of the satisfaction related to getting answers for a question and the satisfaction related to getting comments for an opinion. The loyalty of an agent increases if the agent gets good-quality answers or receives many reponses for the opinion he posted.
B. Activation module The activation module generates the action set that will be executed by the action module. The action set includes initiation actions and response actions.
Difficulty level of a question = f (expertise level) D. Adaptation module
Improvement in expertise = f (total messages read, average quality of answers)
Action set = initiation actions + response actions The immediate learning incentives determine whether the agent asks questions or expresses opinions, or both.
The expertise level is incresed when the agent reads messages and get answers for the question posted. Loyalty = intellectual gain + social gain
Initiation actions = f (immediate learning incentives) Intellectual gain = f (improvement in expertise) The response actions, including answering questions and commenting on opinions, are determined by the perceptions, and both cognitive and social characteristics. Response actions = questions to answer + opinions to comment on Before answering a question, the agent looks at the current answers. The quality of the current answer, the expertise of the agent, and the activeness of the agent will determine whether the agent answers the question. The number of the current comments, and the activeness of the agent will determine whether the agent will comment on an opinion or not. The reason to take into account the
Social gain = f (the number of replying messages) Two situations will increase the loyalty of an agent: getting high quality answers for the questions or receiving many replies for the opinions he posted.
4. Simulation of the VLC behavior The agent-based model is implemented using a software package, ZEUS toolkit. The ZEUS was developed by the British Telecom (http://more.btexact.com/projects/agents.htm) and provides a library of software components and tools that
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facilitate the rapid design, development and deployment of agent systems. The objective of the simulation is to generate the basic patterns of the community development, and understand the relationship between the participant and the community. The development of virtual communities has been operationized to certain measurable parameters, such as number of community participants, time spent per community participant, number of discussion threads, etc. Our goal is to observe the following. • The growth in the total number of participants • The distribution of different types of participants • Ability of the system to reach a stable stage (stable number of participants) • Percentage of different types of participants when the system reaches its stable state • The contributions from different types of participants
4.1 Assumptions and system parameters
Table 2.Major parameters of the benchmark simulation 1
2
4 any
3
High
5 : 10 : 5
