A domain specific language for complex dynamic decision making

Effective decision making of organisation requires deep understanding of various organisational aspects such as its goals, structure, business-as-usual operational processes in the context of dynamic, socio-technical and uncertain business envi-ronment. Decision making approaches adopt a range of modelling and analysis techniques for effective decision making. The current state-of-practice of deci-sion-making typically relies heavily on human experts using intuition aided by ad-hoc representation of an organisation. Existing technologies for decision mak-ing are not able to represent all constructs that are needed for effective decision making nor do they comprehensively address the analysis needs. This paper pro-poses a meta-model to represent organisation and decision artifacts in a compre-hensive, relatable and analysable form that serves as a basis for a domain specific language (DSL) for complex dynamic decision making. The efficacy of the pro-posed meta-model as regards specification and analysis is evaluated using a real-life scenario

Actor based behavioural simulation as an aid for organisational decision making

Decision-making is a critical activity for most of the modern organizations to stay competitive in rapidly changing business environment. Effective organisational decision-making requires deep understanding of various organisational aspects such as its goals, structure, business-as-usual operational processes, environment where it operates, and inherent characteristics of the change drivers that may impact the organisation. The size of a modern organisation, its socio-technical characteristics, inherent uncertainty, volatile operating environment, and prohibitively high cost of the incorrect decisions make decision-making a challenging endeavor. While the enterprise modelling and simulation technologies have evolved into a mature discipline for understanding a range of engineering, defense and control systems, their application in organisational decision-making is considerably low. Current organisational decision-making approaches that are prevalent in practice are largely qualitative. Moreover, they mostly rely on human experts who are often aided with the primitive technologies such as spreadsheets and visual diagrams. This thesis argues that the existing modelling and simulation technologies are neither suitable to represent organisation and decision artifacts in a comprehensive and machine-interpretable form nor do they comprehensively address the analysis needs. An approach that advances the modelling abstraction and analysis machinery for organisational decision-making is proposed. In particular, this thesis proposes a domain specific language to represent relevant aspects of an organisation for decision-making, establishes the relevance of a bottom-up simulation technique as a means for analysis, and introduces a method to utilise the proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner

A model based approach for complex dynamic decision-making

Current state-of-the-practice and state-of-the-art of decision-making aids are inadequate for modern organisations that deal with significant uncertainty and business dynamism. This paper highlights the limitations of prevalent decision-making aids and proposes a model-based approach that advances the modelling abstraction and analysis machinery for complex dynamic decision-making. In particular, this paper proposes a meta-model to comprehensively represent organisation, establishes the relevance of model-based simulation technique as analysis means, introduces the advancements over actor technology to address analysis needs, and proposes a method to utilise proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner. The proposed approach is illustrated using a near real-life case-study from a business process outsourcing organisation

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar

OrgML - a domain specific language for organisational decision-making

Effective decision-making based on precise understanding of an organisation is critical for modern organisations to stay competitive in a dynamic and uncertain business environment. However, the state-of-the-art technologies that are relevant in this context are not adequate to capture and quantitatively analyse complex organisations. This paper discerns the necessary information for an organisational decision-making from management viewpoint, discusses inadequacy of the existing enterprise modelling and specification techniques, proposes a domain specific language to capture the necessary information in machine processable form, and demonstrates how the collected information can be used for a simulation-based evidence-driven organisational decision-making

OrgML - a domain specific language for organisational decision-making

Effective decision-making based on precise understanding of an organisation is critical for modern organisations to stay competitive in a dynamic and uncertain business environment. However, the state-of-the-art technologies that are relevant in this context are not adequate to capture and quantitatively analyse complex organisations. This paper discerns the necessary information for an organisational decision-making from management viewpoint, discusses inadequacy of the existing enterprise modelling and specification techniques, proposes a domain specific language to capture the necessary information in machine processable form, and demonstrates how the collected information can be used for a simulation-based evidence-driven organisational decision-making

Digital twin as risk-free experimentation aid for techno-socio-economic systems

Environmental uncertainties and hyperconnectivity force techno-socio-economic systems to introspect and adapt to succeed and survive. Current practice is chiefly intuition-driven which is inconsistent with the need for precision and rigor. We propose that this can be addressed through the use of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free ‘in silico’ experimentation aid to help: (i) understand why system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have greater impact potential. Our novel approach contributes a meta model for simulatable digital twin of industry scale techno-socio-economic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss validation of this approach, associated technology infrastructure, and architecture through a representative sample of industry-scale real-world use cases

A method for effective use of enterprise modelling techniques in complex dynamic decision making

Effective organisational decision-making requires information pertaining to various organisational aspects, precise analysis capabilities, and a systematic method to capture and interpret the required information. The existing Enterprise Modelling (EM) and actor technologies together seem suitable for the specification and analysis needs of decision making. However, in absence of a method to capture required information and perform analyses, the decision-making remains a complex endeavour. This paper presents a method that captures required information in the form of models and performs what-if calculations in a systematic manner

An actor-model based bottom-up simulation - An experiment on Indian demonetisation initiative

The dominance of cash-based transactions and relentless growth of a shadow economy triggered a fiscal intervention by the Indian government wherein 86% of the total cash in circulation was pulled out in a sudden announcement on November 8, 2016. This disruptive initiative resulted into prolonged cash shortages, financial inconvenience, and crisis situation to cross-section of population of the country. Overall, the initiative has faced a lot of criticism as being poorly thought through and inadequately planned. We claim that these emerging adverse conditions could have been anticipated well in advance with appropriate experimental setup. We further claim that the efficacy of possible courses of actions for managing critical situations, and probable consequences of the courses of action could have been estimated in a laboratory setting. This paper justifies our claims with an experimental setup relying on what-if analysis using an actor-based bottom up simulation approach