From Organisational Structure to Organisational Behaviour Formalisation
- URL: http://arxiv.org/abs/2109.14381v1
- Date: Wed, 29 Sep 2021 12:32:10 GMT
- Title: From Organisational Structure to Organisational Behaviour Formalisation
- Authors: Catholijn M. Jonker and Jan Treur
- Abstract summary: This paper addresses the question how these two perspectives can be combined in one framework.
It is shown how for different aggregation levels and other elements within an organisation structure, sets of dynamic properties can be specified.
- Score: 5.916566419205907
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To understand how an organisational structure relates to organisational
behaviour is an interesting fundamental challenge in the area of organisation
modelling. Specifications of organisational structure usually have a
diagrammatic form that abstracts from more detailed dynamics. Dynamic
properties of agent systems, on the other hand, are often specified in the form
of a set of logical formulae in some temporal language. This paper addresses
the question how these two perspectives can be combined in one framework. It is
shown how for different aggregation levels and other elements within an
organisation structure, sets of dynamic properties can be specified.
Organisational structure provides a structure of (interlevel) relationships
between these multiple sets of dynamic properties. Thus organisational
structure is reflected in the formalisation of the dynamics of organisational
behaviour. To illustrate the effectiveness of the approach a formal foundation
is presented for the integrated specification of both structure and behaviour
of an AGR organisation model.
Related papers
- Structured Active Inference (Extended Abstract) [0.0]
We introduce structured active inference, a large generalization and formalization of active inference using the tools of categorical systems theory.
We cast generative models formally as systems "on an interface", with the latter being a compositional abstraction of the usual notion of Markov blanket.
Agents are then 'controllers' for their generative models, formally dual to them.
arXiv Detail & Related papers (2024-06-07T17:22:44Z) - Learning Correlation Structures for Vision Transformers [93.22434535223587]
We introduce a new attention mechanism, dubbed structural self-attention (StructSA)
We generate attention maps by recognizing space-time structures of key-query correlations via convolution.
This effectively leverages rich structural patterns in images and videos such as scene layouts, object motion, and inter-object relations.
arXiv Detail & Related papers (2024-04-05T07:13:28Z) - Document Structure in Long Document Transformers [64.76981299465885]
Long documents often exhibit structure with hierarchically organized elements of different functions, such as section headers and paragraphs.
Despite the omnipresence of document structure, its role in natural language processing (NLP) remains opaque.
Do long-document Transformer models acquire an internal representation of document structure during pre-training?
How can structural information be communicated to a model after pre-training, and how does it influence downstream performance?
arXiv Detail & Related papers (2024-01-31T08:28:06Z) - Semantic Computing for Organizational Effectiveness: From Organization
Theory to Practice through Semantics-Based Modelling [0.0]
Key features of our model include inferable dependencies, explainable coordination and cooperation risks, and actionable insights on how dependency structures within an organization can be altered to mitigate the risks.
Our approach underscores the transformative potential of semantics in deriving tangible, real-world value from existing organization theory.
arXiv Detail & Related papers (2023-12-29T19:37:35Z) - StructRe: Rewriting for Structured Shape Modeling [63.792684115318906]
We present StructRe, a structure rewriting system, as a novel approach to structured shape modeling.
Given a 3D object represented by points and components, StructRe can rewrite it upward into more concise structures, or downward into more detailed structures.
arXiv Detail & Related papers (2023-11-29T10:35:00Z) - The Analysis and Extraction of Structure from Organizational Charts [0.0]
Organizational charts, also known as org charts, are critical representations of an organization's structure and the hierarchical relationships between its components and positions.
We present an automated and end-to-end approach that uses computer vision, deep learning, and natural language processing techniques.
arXiv Detail & Related papers (2023-11-16T23:49:05Z) - Query Structure Modeling for Inductive Logical Reasoning Over Knowledge
Graphs [67.043747188954]
We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs.
It encodes linearized query structures and entities using pre-trained language models to find answers.
We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
arXiv Detail & Related papers (2023-05-23T01:25:29Z) - Semantic Structure Enhanced Event Causality Identification [57.26259734944247]
Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts.
Existing methods underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure.
arXiv Detail & Related papers (2023-05-22T07:42:35Z) - Autoregressive Structured Prediction with Language Models [73.11519625765301]
We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs.
Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
arXiv Detail & Related papers (2022-10-26T13:27:26Z) - Decomposing and Recomposing Event Structure [4.270553193574436]
We induce this jointly with role, entity type and event-level semantic graphs.
We identify sets of types that align closely with previous theoretically-motivated document-level generative models.
arXiv Detail & Related papers (2021-03-18T17:16:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.