This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.

This chapter describes a second-order adaptive network model for mental processes making use of shared mental models (SMM) for team performance. The chapter illustrates on the one hand the value of adequate SMM’s for safe and efficient team performance and on the other hand in cases of imperfections of such shared team models how this complicates the team performance. To this end, the adaptive network model covers use, adaptation and control of the shared mental model. It is illustrated for an application context of a medical team performing a tracheal intubation, executed by a nurse and a medical specialist. Simulations illustrate how the adaptive network model is able to address the type of complications that can occur in realistic scenarios.

Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient care by reducing medical error risk. Speaking up by healthcare professionals (e.g., nurses, residents) remains difficult to execute in practice despite increasing awareness of its importance. Therefore, this paper discourses a computational model concerning the mechanisms known from psychological, observational, and medical literature which underlie the speaking up behaviour of a health care professional. It also addresses how a doctor may respond to the communicated message. Through several scenarios we illustrate what pattern of factors causes a healthcare professional to speak up when witnessing a (near) medical error. We moreover demonstrate how introducing an observant agent can facilitate effective communication and help to ensure patient safety through speaking up when a nurse can not. In conclusion, the current paper introduces an adaptive computational model which predicts speaking up behaviour from the perspective of the speaker and receiver, with the addition of a virtual coach to further optimise patient safety when a patient could be in harm's way.

This paper describes an extension of a safety culture within hospital organizations providing more transparency and acknowledgement of all actors, and in particular the parents. It contributes a model architecture to support a hospital to develop such an extended safety culture. It is illustrated for prevention of postpartum depression. Postpartum depression is a commonly known consequence of childbirth for both mothers and fathers. In this research, we computationally analyze the risk factors and lack of support received by fathers. Therefore, we use shared mental models to model the effects of poor and additional communication by healthcare practitioners to mitigate the development of postpartum depression in both the mother and the father. Both individual mental models and shared mental models are considered in the design of the computational model. The paper illustrates the benefits of simple support in terms of communication during childbirth, which has lasting effects, even outside the hospital. For the impact of additional communication, a Virtual Safety Coach is designed that intervenes when necessary to provide support, i.e., when a health care practitioner doesn't. Moreover, organizational learning is also modelled to improve the mental models of both the Safety Coach and the Health Care Practitioner.

This paper addresses formalization and computational modeling of multilevel organizational learning, which is one of the major challenges for the area of organizational learning. It is discussed how various conceptual mechanisms in multilevel organizational learning as identified in the literature, can be formalized by computational mechanisms which provide mathematical formalizations that enable computer simulation. The formalizations have been expressed using a self-modeling network modeling approach.

This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organizational learning as an alternative route, which includes feed forward and feedback learning. The results show that effective communication (triggered by the active team leader, and/or by natural, informal communication), leads to a faster learning process within an organization compared to the longer route via feed forward and feedback formal organisational learning. However, this more direct form of bilateral learning in general may take more of the employee's time, as a quadratic number of dyadic interactions in general is less efficient than a linear number of interactions needed for feed forward and feedback organisational learning.

This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To this end a second-order adaptive self-modelling network model is introduced and an example simulation for the case study is discussed.

This chapter describes a second-order adaptive network model for mental processes making use of shared mental models (SMM) for team performance. The chapter illustrates on the one hand the value of adequate SMM’s for safe and efficient team performance and on the other hand in cases of imperfections of such shared team models how this complicates the team performance. To this end, the adaptive network model covers use, adaptation and control of the shared mental model. It is illustrated for an application context of a medical team performing a tracheal intubation, executed by a nurse and a medical specialist. Simulations illustrate how the adaptive network model is able to address the type of complications that can occur in realistic scenarios.

Multilevel organizational learning concerns an interplay of different types of learning at individual, team, and organizational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.

This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this, as a form of further innovation three new features have been added to the modeling approach: Recombination of selected high-quality mental model parts, refinement of mental model parts, and distinction between context-sensitive detailed control and global control.

Religion is an important aspect of many individuals’ lives. A central element of it is a mental God-model. Such a mental God-model can have different forms and depending on that it has a specific influence on human behaviour. This has been extensively studied from different perspectives. The current study uses a number of these perspectives as a basis for an integrative adaptive network model for formation and use of a mental God-model. It describes the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. Based on the obtained network model, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus here was mainly on the very relevant topic of the influence of religion on human empathy and disempathy. The developed adaptive network model can be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals and its underlying mechanisms. It is illustrated for a number of different scenarios for different types of persons and religions.

In this paper, equilibrium analysis for network models is addressed and applied in particular to a network model of multilevel organisational learning. The equilibrium analysis addresses properties of aggregation characteristics and connectivity characteristics of a network. For aggregation characteristics, it is shown how certain classes of nonlinear functions enable equilibrium analysis of the emerging dynamics within the network like linear functions do. For connectivity characteristics, by using a form of stratification for the network's strongly connected components, it is shown how equilibrium analysis results can be obtained relating equilibrium values in any component to equilibrium values in (independent) components without incoming connections. In addition, concerning aggregation characteristics, two specific types of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are analysed. It is illustrated in detail how by using certain function transformations also methods for equilibrium analysis based on a symbolic linear equation solver, can be applied to make predictions about equilibrium values for them. All these results are applied to a network model for organisational learning. Finally, it is analysed in some depth how the function transformations applied can be described by the more general notion of function conjugate relation, also often used for coordinate transformations.

This paper describes a second-order adaptive network model for mental processes making use of shared mental models for team performance. The paper illustrates the value of adequate shared mental models for safe and efficient team performance and in cases of imperfections of such shared team models how this complicates the team performance. It is illustrated for a context of a medical team performing a tracheal intubation. Simulations illustrate how the adaptive network model is able to address the type of complications that can occur in realistic scenarios.

Chronic lifestyle-related disease is one of the major health problems the world is facing in the twenty-first century. One of the ways to address the problem of chronic lifestyle-related disease is through programs which support lifestyle changes. Research shows the actual benefits of lifestyle changes for patients. However, in reality only a fraction of patients succeeds in changing their lifestyle. Their level and type of motivation appear to be important factors in shaping their chances for success. We devised a second order temporal-causal network for adoption of habit with regards to motivation, focusing on patients with type 2 diabetes who receive advice to exercise. Taking into account a variety of motivational forces, the model clearly shows the benefits of more and repeated interactions. They lead to a more long-lasting change, even though these interactions may also amplify negative emotions. These findings are in accordance with tentative findings in research.

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.

In this paper counterfactual thinking is addressed based on literature mainly from Neuroscience and Psychology. A detailed literature review was conducted in identifying processes, neural correlates and theories related to counterfactual thinking from different disciplines. A familiar scenario with respect to counterfactual thinking was identified. Based on the literature, an adaptive self-modeling network model was designed. This model captures the complex process of counterfactual thinking and the learning and control involved.

This paper describes a network model for mental processes making use of shared mental models (SMM) of team performance. The paper illustrates the value of adequate SMM’s for safe and efficient team performance. The addressed application context is that of a medical team performing a tracheal intubation executed by a nurse and a medical specialist. Simulations of successful and unsuccessful team performance have been performed, some of which are presented. The paper discusses potential further elaborations for future research as well as implications for other domains of team performance.

A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a "guessing" heuristic, by which an agent uses the history of the opponent's bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.