Confronting Project Conflicts into Success: a Complex Systems Design Approach to Resolving Stalemates
- URL: http://arxiv.org/abs/2409.10549v1
- Date: Mon, 2 Sep 2024 07:44:43 GMT
- Title: Confronting Project Conflicts into Success: a Complex Systems Design Approach to Resolving Stalemates
- Authors: L. G. Teuber, A. R. M. Wolfert,
- Abstract summary: In today's complex projects development, stakeholders are often involved too late.
A purely associative and a-priori design-supported approach integrates both system's reality and stakeholder's interests.
The state-of-the-art Preferendus is deployed to co-creatively generate a best-fit-for-common-purpose solution.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In today's complex projects development, stakeholders are often involved too late. There is also in many cases a one-sided technical focus that only focuses on the system's behaviour and does not integrate the individual stakeholder preferences. This locks stakeholders into a 'technical' conflict instead of being able to emerge from it 'socially'. Moreover, stakeholders are often involved a-posteriori in a multi-faceted development process which is untransparent, leading to stalemates or even artefacts that nobody ever wants. There is thus a need for a purely associative and a-priori design-supported approach that integrates both system's reality and stakeholder's interests within a joint agreement and technical framework. The state-of-the-art Preferendus, the computer-aided design engine embedded within the proven Open Design Systems (Odesys) methodology, is a neutral tool in confronting complexity into success. The Preferendus is deployed to co-creatively generate a best-fit-for-common-purpose solution for a number of wind farm related degrees of freedom, project constraints and given a number of stakeholder objective functions. Since, the Preferendus design potential for a stalemate depends strongly on stakeholder interest, importance and trust, in this paper an structured stakeholder judgement approach is introduced to transparently arrive at individual stakeholder weights using a choice-based conjoint analysis (CBCA) method. This method also allows for obtaining an initial estimate for the individual stakeholder preference functions. By modelling disputable exogenous factors as endogenous design parameters, it is also shown for which factors the stalemate problem is indeed both technically and socially (un)solvable, while interests and reality are conjoined.
Related papers
- Agentic LLM Framework for Adaptive Decision Discourse [2.4919169815423743]
This study introduces a real-world inspired agentic Large Language Models (LLMs) framework.
Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents.
Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways.
arXiv Detail & Related papers (2025-02-16T03:46:37Z) - Exploring near-optimal energy systems with stakeholders: a novel approach for participatory modelling [41.94295877935867]
Participatory research in energy modelling offers the opportunity to engage with stakeholders in a comprehensive way.
We present a methodology and a framework, based on near-optimal modelling results, that can incorporate stakeholders in a holistic way.
We showcase the methodology for the remote Arctic settlement of Longyearbyen and illustrate how participants deviate consistently from the cost optimum.
arXiv Detail & Related papers (2025-01-09T14:43:29Z) - Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment [58.049113055986375]
We develop a single stage approach named Alignment with Integrated Human Feedback (AIHF) to train reward models and the policy.
The proposed approach admits a suite of efficient algorithms, which can easily reduce to, and leverage, popular alignment algorithms.
We demonstrate the efficiency of the proposed solutions with extensive experiments involving alignment problems in LLMs and robotic control problems in MuJoCo.
arXiv Detail & Related papers (2024-06-11T01:20:53Z) - IRatePL2C: Importance Rating-based Approach for Product Lines Collaborative Configuration [0.6091702876917281]
IRatePL2C is a resolution strategy that relies on importance degrees assigned by the stakeholders to their initial configuration choices.
An illustrative example is presented to evaluate the approach.
arXiv Detail & Related papers (2024-04-27T11:17:01Z) - Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver.
This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
arXiv Detail & Related papers (2023-01-28T01:50:42Z) - Interactive Evolutionary Multi-Objective Optimization via
Learning-to-Rank [8.421614560290609]
This paper develops a framework for designing preference-based EMO algorithms to find solution(s) of interest (SOI) in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates.
By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm.
arXiv Detail & Related papers (2022-04-06T06:34:05Z) - Reinforcement Learning for Flexibility Design Problems [77.37213643948108]
We develop a reinforcement learning framework for flexibility design problems.
Empirical results show that the RL-based method consistently finds better solutions than classical methods.
arXiv Detail & Related papers (2021-01-02T02:44:39Z) - End-to-End Learning and Intervention in Games [60.41921763076017]
We provide a unified framework for learning and intervention in games.
We propose two approaches, respectively based on explicit and implicit differentiation.
The analytical results are validated using several real-world problems.
arXiv Detail & Related papers (2020-10-26T18:39:32Z) - What If I Don't Like Any Of The Choices? The Limits of Preference
Elicitation for Participatory Algorithm Design [12.386462516398469]
We argue that optimizing for individual preference satisfaction in the distribution of limited resources may actually inhibit progress towards social and distributive justice.
Individual preferences can be a useful signal but should be expanded to support more expressive and inclusive forms of democratic participation.
arXiv Detail & Related papers (2020-07-13T21:58:30Z) - Decentralized Reinforcement Learning: Global Decision-Making via Local
Economic Transactions [80.49176924360499]
We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems.
We derive a class of decentralized reinforcement learning algorithms.
We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
arXiv Detail & Related papers (2020-07-05T16:41:09Z)
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.