Organizational Learning and Social Computation

2007 ◽  
pp. 294-317
Author(s):  
Luca Iandoli ◽  
Giuseppe Zollo
Keyword(s):  
Social Network ◽  
Organizational Learning ◽  
Personal Characteristics ◽  
Learning Processes ◽  
Organizational Analysis ◽  
Initial State ◽  
Shared Experience ◽  
Agent Based ◽  
Proposed Model ◽  
Exchange Information

In Chapter XVIII we outlined the characteristics of a computational approach to support organizational analysis. Agent-based modeling, one of the several methodological tools presented in Chapter XVIII, is particularly suited for the modeling of learning processes in complex networks. In this appendix we want to provide the reader with an example of how it is possible to construct agent-based systems in order to simulate the collective behavior of social aggregates. We present a mathematical model aimed to represent and simulate adaptive organizational learning processes. With adaptive organizational learning processes we mean a learning process taking place in a social network in which individuals, by means of social interaction and subjective interpretative processes, contribute to the construction and the accumulation of shared experience. The proposed model implements a multiagent system aimed to represent a social network of interacting heterogeneous ‘virtual people’ operating in a virtual environment, here modeled as a network of resources. Learning for an agent means passing from an initial state to a target one through the identification of optimal paths within the environment by exploiting personal characteristics as well as interaction with other agents and the environment; such interaction allows agents to exchange information, to construct a collective memory on the basis of past individual experiences and to have access to resources.

scholarly journals Social Network Approach To Organizational Learning

2011 ◽  
Vol 22 (2) ◽  
Author(s):  
Miha Škerlavaj ◽  
Vlado Dimovski
Keyword(s):  
Social Network ◽  
Organizational Learning ◽  
Organizational Performance ◽  
Research Question ◽  
Personal Characteristics ◽  
Network Size ◽  
Software Company ◽  
Knowledge Based ◽  
Social Network Approach ◽  
Performance Research

Organizational learning is one of the most promising concepts and propulsive areas of research in modern managerial literature. So far, it was proved that higher-level organizational learning contributes to organizational performance. Research question that remains explained inadequately is how learning occurs. Based on exploratory social network analysis conducted on a learning network within a software company, we offer eight propositions. First, the greater the experience of the employee in a certain field, the bigger the probability that co-workers will seek to learn from this person. Then, respectively, physical proximity (e.g. shared office or geographical proximity), similarity in level of expertise, and complementarities in personal characteristics augment the probability that co-workers will learn from each-other. Next, network size and age affect its density. Finally, knowledge-based organizations seem to have cohesive structure of relationships among their members. Further confirmatory research is needed in order to develop and test propositions offered.

Organizational Learning and Multiteam Systems

2019 ◽  
pp. 602-620
Author(s):  
Leslie A. DeChurch ◽  
Gina M. Bufton ◽  
Sophie A. Kay ◽  
Chelsea V. Velez ◽  
Noshir Contractor
Keyword(s):  
Organizational Learning ◽  
Knowledge Transfer ◽  
Knowledge Creation ◽  
Learning Processes ◽  
Organizational Systems ◽  
System Perspective ◽  
Multiteam Systems ◽  
Starting Point ◽  
Emergent States ◽  
Superordinate Goal

Multiteam systems consist of two or more teams, each of which pursues subordinate team goals, while working interdependently with at least one other team toward a superordinate goal. Many teams work in these larger organizational systems, where oft-cited challenges involve learning processes within and between teams. This chapter brings a learning perspective to multiteam systems and a multiteam system perspective to organizational learning. Several classic illustrations of organizational learning—for example, the Challenger and Columbia disasters—actually point to failures in organizational learning processes within and between teams. We offer the focus on intrateam knowledge creation and retention and interteam knowledge transfer as a useful starting point for thinking about how to conceptually and operationally define learning in multiteam systems. Furthermore, we think leadership structures and multiteam emergent states are particularly valuable drivers of learning.

scholarly journals A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4636
Author(s):  
Mohammed Elhenawy ◽  
Mostafizur R. Komol ◽  
Mahmoud Masoud ◽  
Shiqiang Liu ◽  
Huthaifa I. Ashqar ◽  
...  
Keyword(s):  
User Needs ◽  
Computational Results ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Ride Sharing ◽  
Proposed Model ◽  
Micro Mobility

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

scholarly journals Stress-free state of the red blood cell membrane and the deformation of its skeleton

2012 ◽  
Vol 17 (2) ◽  
Author(s):  
Tjaša Švelc ◽  
Saša Svetina
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Variational Calculation ◽  
Lateral Distribution ◽  
Free State ◽  
Initial State ◽  
Proposed Model ◽  
Deformed State ◽  
Concentric Circles ◽  
Stress Free State

AbstractThe response of a red blood cell (RBC) to deformation depends on its membrane, a composite of a lipid bilayer and a skeleton, which is a closed, twodimensional network of spectrin tetramers as its bonds. The deformation of the skeleton and its lateral redistribution are studied in terms of the RBC resting state for a fixed geometry of the RBC, partially aspirated into a micropipette. The geometry of the RBC skeleton in its initial state is taken to be either two concentric circles, a references biconcave shape or a sphere. It is assumed that in its initial state the skeleton is distributed laterally in a homogeneous manner with its bonds either unstressed, presenting its stress-free state, or prestressed. The lateral distribution was calculated using a variational calculation. It was assumed that the spectrin tetramer bonds exhibit a linear elasticity. The results showed a significant effect of the initial skeleton geometry on its lateral distribution in the deformed state. The proposed model is used to analyze the measurements of skeleton extension ratios by the method of applying two modes of RBC micropipette aspiration.

Coordinating Human and Machine Learning for Effective Organization Learning

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1581-1602
Author(s):  
Timo Sturm ◽  
◽  
Jin Gerlacha ◽  
Luisa Pumplun ◽  
Neda Mesbah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organizational Learning ◽  
Organization Learning ◽  
Agent Based ◽  
Scarce Resources ◽  
Turbulent Environments ◽  
Explorative Learning ◽  
New Ideas ◽  
Effective Organization ◽  
Stock Of Knowledge

With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals Agent Based Load Balancing in Grid Computing

2020 ◽  
Author(s):  
Wided Ali ◽  
Fatima Bouakkaz
Keyword(s):  
Load Balancing ◽  
Heterogeneous Systems ◽  
Agent Based ◽  
Selection Policy ◽  
Development Framework ◽  
Policy Selection ◽  
Proposed Model ◽  
Multi Agent ◽  
Location Policy ◽  
System Characteristics

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

scholarly journals The Safety of Professionalization Subjects in Psychological and Neuropsychological Aspects

2021 ◽  
Vol 12 (1) ◽  
pp. 19-39
Author(s):  
Olha Lazorko ◽  
Virna Zhanna ◽  
Vasyl Yahupov ◽  
Oksana Valchuk-Orkusha ◽  
Iryna Melnyk ◽  
...  
Keyword(s):  
Professional Training ◽  
Functional Organization ◽  
Personal Characteristics ◽  
Personal Protection ◽  
Proposed Model ◽  
Security Problem ◽  
The Subject ◽  
Methodological Principles ◽  
The Individual ◽  
Graduating Students

Recently, the revision of priorities in the interpretation of the security problem and their transformation from the interests of the state to the interests of man himself, have actualized the study of psychological protection. Especially aspects of personal protection are relevant in the professional sphere, which led to the development of the problem of personal protection as a subject of professionalization, taking into account psychological and neuropsychological factors. The purpose of the study is to empirically verify the structurally functional organization of personal protection as a subject of professionalization. The proposed model is based on the methodological principles and conditions of the content of the subject, system and synergetic approaches (the subject principle determines the subjective features expressed in subjective-personal characteristics, the system principle - substantial features expressed in socially personal characteristics; the synergetic principle - quality features that are integrative sign of professional protection of the individual. The sample of the study was: graduating students (n = 180); 4th and 5th year students (n = 230); doctors and medical workers (n = 441). The characteristics of psychodiagnostic tools used in these blocks of the empirical research program are described. The results of the study demonstrated the excellent content of empirical referents of professional protection of the individual in the period of professional optation, professional training and actual professional implementation in ordinary and special conditions of activity. The scientific position of the empirical study of professional safety of the individual is realized in the separation of the experience of social satisfaction, the system-forming factor of which is the urgent need that initiates the manifestation of successful professional realization.

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