Figure 4 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Challenges regarding cognitive cloning: a) explicit (donor to clone) knowledge transfer (e.g., as a set of decision rules); b) machine learning-driven training of the clone (donor labels the particular decision contexts, and the clone learns the boundaries between different decision options by discovering the hidden decision rules of the donor); c) adversarial learning (driven by GANs) helps facilitate the training process in b) by discovering the corner cases for challenging decisions, hence making the clone's decision boundaries and rules closer to the donor's; d) discovering and making explicit the contexts that influence the donor's decision boundaries and rules and training the clone specifically for all such contexts; e) integrating both explicit and learned decision knowledge into different decision tasks and decision contexts under the umbrella of personal decision ontology, which will be used by the Pi-Mind agent when acting as a clone of a particular human donor.
Source publication
This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their “do...
Contexts in source publication
Context 1
... us assume that we want to train the clone to make the same decisions as the donor when facing the same decision problems within the same decision context in the future. Figure 4(a) illustrates a simple explicit donor-clone knowledge transfer case. Here, we have a two-dimensional decision space, meaning that each decision task is defined by two parameters (x, y). ...
Context 2
... Figure 4(a), the donor is supposed to know the rules for addressing particular types of decision cases. Each rule is a kind of formal definition of the bounded subspaces within the decision space, corresponding to each decision option (the area of "YES" decisions and the area of "NO" decisions in the figure). ...
Context 3
... collected samples can then be used as training data for various computational intelligence techniques, drawing the decision boundaries and capturing the rules for designing the clone in a bottom-up (ML-driven) way. This option is illustrated in Figure 4(b). Here, based on several cases of "YES" and "NO" decisions, some ML algorithms draw the decision boundary (the separation curve between the "YES" and "NO" decision subspaces). ...
Context 4
... supervised ML depends heavily on the training data. Let us assume that the actual (but hidden) decision boundary for a particular donor is the line shown in Figure 4(a). However, some ML algorithms (e.g., neural network backpropagation learning) draw the curve as the decision boundary based on the labelled data, as shown in Figure 4(b). ...
Context 5
... us assume that the actual (but hidden) decision boundary for a particular donor is the line shown in Figure 4(a). However, some ML algorithms (e.g., neural network backpropagation learning) draw the curve as the decision boundary based on the labelled data, as shown in Figure 4(b). The donor will continue to make further decisions with different boundaries from the clone. ...
Context 6
... donor will continue to make further decisions with different boundaries from the clone. As shown in Figure 4(c), the difference between actual and guessed decision boundaries creates some divergence areas within the decision space; if they happen to belong to these areas, all the decision tasks will be addressed differently by the donor and clone. To minimise discrepancies between the donor's and clone's opinions, we have to provide the clone with better training data. ...
Context 7
... used GANs for this purpose because they are capable of discovering (within the real-time training process) divergence areas and generating new (corner) cases (to be labelled by the donor) from these areas. This type of (adversarial) training, as illustrated in Figure 4(c), facilitates the learning of precise decision boundaries and makes the clone capable of making almost the same decisions as the donor would. g., as a set of decision rules); b) machine learning-driven training of the clone (donor labels the particular decision contexts, and the clone learns the boundaries between different decision options by discovering the hidden decision rules of the donor); c) adversarial learning (driven by GANs) helps facilitate the training process in b) by discovering the corner cases for challenging decisions, hence making the clone's decision boundaries and rules closer to the donor's; d) discovering and making explicit the contexts that influence the donor's decision boundaries and rules and training the clone specifically for all such contexts; e) integrating both explicit and learned decision knowledge into different decision tasks and decision contexts under the umbrella of personal decision ontology, which will be used by the Pi-Mind agent when acting as a clone of a particular human donor. ...
Context 8
... if the clone learns the donor's decision logic (appropriate decision boundaries and corresponding decision rules) in one context, this will not mean that the same logic would work in another context for the same set of decision tasks. Figure 4(d) illustrates the context-dependent decision boundaries and the appropriate (meta-) rules. When a particular decision context is known, the particular decision boundary (and hence the corresponding decision rules) becomes valid (according to explicitly defined meta-rules) and will be used for further decisions. ...
Context 9
... these decision-making skills, which the intended clone either gets explicitly from the donor or learns by observing the donor's decisions for different decision problems and contexts, must be integrated as an interconnected set of capabilities controlled by the Pi-Mind agent. This content (the taxonomies and semantic graphs of acquired or learned decision problems, parameters, options, contexts, boundaries, rules, and meta-rules that characterise the decision-making specifics of the donor) is constructed automatically under the umbrella of the personal decision ontology, as shown in Figure 4(e). Semantic (machine-processable) representation on the top of decision models allows for automated processing (by the Pi-Mind agent-driven clone), seamless integration of available decision-making knowledge and skills, openness to lifelong learning of new knowledge and skills (via continuous observation of the donor), and communication and coordination between intelligent agents. ...
Citations
... Golovianko, M., Gryshko, S., Terziyan, V., and Tuunanen, T. [199] 2022 ...
... An innovative machine learning model for supply chain management. Lin, H., Lin, J., and Wang, F. [200] In [199], a technology robot called Pi-Mind is developed and evaluated; it acts as a responsible, resilient, ubiquitous cognitive clone (or a digital copy) and as an autonomous representative of a human decision-maker. To train the Pi-Mind agent to choose the most appropriate solution from among alternatives at critical decision points, the authors train agents using GANs. ...
Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics.
... In [180] a technology robot, called Pi-Mind, is developed and evaluated that acts as a responsible, resilient, ubiquitous cognitive clone (or a digital copy) and as an autonomous representative of a human decision-maker. To train the Pi-Mind agent to choose the most appropriate solution from among alternatives at critical decision points, they employ training agents with GANs. ...
Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.
... The challenge is that DSR has thus far primarily concentrated on the formulation of design theories (Miah et al., 2019) based on conventional (i.e., non-AI-based) DSS artefacts (Golovianko et al., 2022, Pan et al., 2021, overlooking increasingly important AIADM systems. Recent research shows the extrapolation of prevailing prescriptive design knowledge from conventional DSS to AIADM systems faces four key design challenges (Hevner & Storey, 2023). ...
... Following the prospect theory (Tversky & Kahneman, 1974), DSS design aimed to mitigate humans' cognitive limitations by focusing on enhancing both primary (action selection) and secondary (protocol selection) decisions (Arnott, 2006, Remus & Kottemann, 1986. Recent work in DSS literature has shown a significant rise in DSR (Arnott & Pervan, 2014), developing design theories (Miah et al., 2019) and contextual DSS artefacts often featuring in European IS (Collins et al., 2010, Golovianko et al., 2022, Klör et al., 2018, Pan et al., 2021, Seidel et al., 2018. ...
... We conducted field experiments to evaluate the model's actual benefits (POV) and interviewed the responsible stakeholders of the organisation to identify both desirable and undesirable consequences of its use (POU) at TBô. We adopted the DSR evaluation approach proposed by Venable et al. (2012) and Nunamaker et al. (2015) and applied by Tuunanen and Peffers (2018), Nguyen et al. (2021), and Golovianko et al. (2022). ...
Artificial intelligence (AI) applications have proliferated, garnering significant interest among information systems (IS) scholars. AI-powered analytics, promising effective and low-cost decision augmentation, has become a ubiquitous aspect of contemporary organisations. Unlike traditional decision support systems (DSS) designed to support decisionmakers with fixed decision rules and models that often generate stable outcomes and rely on human agentic primacy, AI systems learn, adapt, and act autonomously, demanding recognition of IS agency within AI-augmented decision making (AIADM) systems. Given this fundamental shift in DSS; its influence on autonomy, responsibility, and accountability in decision making within organisations; the increasing regulatory and ethical concerns about AI use; and the corresponding risks of stochastic outputs, the extrapolation of prescriptive design knowledge from conventional DSS to AIADM is problematic. Hence, novel design principles incorporating contextual idiosyncrasies and practice-based domain knowledge are needed to overcome unprecedented challenges when adopting AIADM. To this end, we conduct an action design research (ADR) study within an e-commerce company specialising in producing and selling clothing. We develop an AIADM system to support marketing, consumer engagement, and product design decisions. Our work contributes to theory and practice with a set of actionable design principles to guide AIADM system design and deployment.
... Effective decision-making is crucial for developing robust cybersecurity strategies and policies across different sectors [1][2][3][4]. However, the increasing scale and sophistication of cyber threats [5], [6]coupled with the complexities of modern technological environments [7], [8] pose significant challenges for decision-makers. ...
This bibliometric analysis explores research trends and patterns in the intersection of decision-making and cybersecurity. Using Scopus data, we conducted a systematic search and identified 4,637 relevant documents published between 2018-2024. Quantitative analysis reveals rising annual publications with a peak in 2023, the predominance of journal articles, and robust international collaboration networks. China and the USA lead global scientific production. Key topics include risk assessment, network security, decision support systems, and emerging technologies like machine learning and artificial intelligence. Core journals with high citation impact such as IEEE Access and Expert Systems with Applications highlight significant sources of literature. The study provides a holistic overview of the landscape, evolution, contributors, and themes within decision-making and cybersecurity research.
... A future avenue for study could also involve the development of a TINN-driven robust digital twin for a human-robot pair within a cobot system. This scenario envisions the synchronous training and seamless integration of a human twin (Gaffinet et al., 2023) or clone (Golovianko et al., 2023) with the robot's digital twin. Such a complex collective intelligence couple (Gavriushenko, 2020) offers intriguing prospects for future exploration and applications. ...
This paper explores knowledge-informed machine learning and particularly taxonomy-informed neural networks (TINN) to enhance data-driven smart assets' maintenance by contextual knowledge. Focusing on assets within the same class that may exhibit subtle variations, we introduce a weighted Lehmer mean as a dynamic mechanism for knowledge integration. The method considers semantic distances between the asset-in-question and others in the class, enabling adaptive weighting based on behavioural characteristics. This preserves the specificity of individual models, accommodating heterogeneity arising from manufacturing and operational factors. In the adversarial learning context, the suggested method ensures robustness and resilience against adversarial influences. Our approach assumes a kind of federated learning from neighbouring assets while maintaining a detailed understanding of individual asset behaviours within a class. By combining smart assets with digital twins, federated learning, and adversarial knowledge-informed machine learning, this study underscores the importance of collaborative intelligence for efficient and adaptive maintenance strategies in Industry 4.0 and beyond.
... Modelling and simulation enhanced with emergent technologies is a key for efficient Industry 4.0 and beyond, including smart manufacturing [1]. In addition to digital twins [2] simulating a physical entity or operation, we can observe a strong trend towards human-centric cyber-physical production systems [3] driven by mental models and smart operators [4] aiming at human values [5], including digital cognitive clones of humans [6] and groups [7]. ...
... However, we are using the "cloning" term to follow consistently the terminology from our former articles where the ultimate objective was formulated as designing digital cognitive clones of humans (as decision-makers) and groups of collective hybrid intelligence (see, e.g. [12], [7], [13], [6], and [14]). ...
... In [6], we summarized our previous digital cognitive cloning results in a comprehensive study where complex MLdriven cloning (based on T-GAN) has been integrated with the autonomous and ontology-driven Pi-Mind cloning and tested within several application domains. ...
... Others, however, contended that human heuristics remain relevant not only because algorithms may prioritize short-term success (Luca et al., 2016) but also because business progress often outpaces the collection and analysis of data available (Bettis, 2017;Davenport, 2013;van den Broek et al., 2022). While it has also been suggested that decision-making jointly conducted by humans and machines may lead to superior outcomes (Raisch & Fomina, 2024;Sturm et al., 2021;van den Broek et al., 2022), the optimal strategies and critical boundaries of human-machine integration are still not well-understood (Golovianko et al., 2023). ...
... Second, by revealing nuanced insights into how BDA shapes heuristics adaptation to uncertain environments, our study also contributes to the literature on IS and BDA. While prior IS studies have examined the various benefits of using BDA in strategic decision-making, whether BDA creates value in uncertain environments remains underexplored (Benbya et al., 2020;Golovianko et al. 2023;Lyytinen & Grover, 2017;Sharma et al., 2014;van den Broek et al., 2022). Adopting the lens of heuristics adaptation, we offer three important modes through which BDA enables organizations to refine their heuristics for more informed decision-making in uncertain environments (Finding 1). ...
... Moreover, our findings indicate that in dynamic environments, where suitable alternatives at time t may become outdated at time t+1, BDA enables decision makers to efficiently update obsolete alternatives with new, relevant ones (Finding 3). This finding addresses the previously unclear role of BDA in dynamic environments (Golovianko et al., 2023;Sturm et al., 2021) and confirms that BDA creates strategic value by aiding organizations to adapt to such environments more effectively (Haefner et al., 2021;van den Broek et al., 2022). In addition, by differentiating the two dimensions of environmental uncertainty, we also enrich the understanding of the contingent value of BDA in strategic decision-making. ...
... Effective decision-making is crucial for developing robust cybersecurity strategies and policies across different sectors [1][2][3][4]. However, the increasing scale and sophistication of cyber threats [5], [6]coupled with the complexities of modern technological environments [7], [8] pose significant challenges for decision-makers. ...
This bibliometric analysis explores research trends and patterns in the intersection of decision-making and cybersecurity. Using Scopus data, we conducted a systematic search and identified 4,637 relevant documents published between 2018-2024. Quantitative analysis reveals rising annual publications with a peak in 2023, the predominance of journal articles, and robust international collaboration networks. China and the USA lead global scientific production. Key topics include risk assessment, network security, decision support systems, and emerging technologies like machine learning and artificial intelligence. Core journals with high citation impact such as IEEE Access and Expert Systems with Applications highlight significant sources of literature. The study provides a holistic overview of the landscape, evolution, contributors, and themes within decision-making and cybersecurity research.
... The concept is being promoted by the Collective Intelligence research group in collaboration with Adversarial Intelligence research group (jyu.fi/it/en/research/our-active-research/collectiveintelligence). To marry efficiency (Industry 4.0) and human-centricity (Industry 5.0), Terziyan et al. [35] announced a new emergent component for the potential hybrid, which is a digital cognitive clone of a human and related technology for cognitive cloning (Pi-Mind) [36]. Golovianko et al. [37] reported successful experiments for cloning decision-making capabilities of human operators. ...
... We argue that a digital cognitive clone of a human could be a compromise within the "robots vs. humans" dilemma, which can serve as a bridging concept to marry automation-centric Industry 4.0 and human-centric Industry 5.0. Appropriate digital cognitive cloning could be performed using, e.g., the Pi-Mind adversarial learning technology, which has been successfully tested in our previous work ( [35], [36] and [37]). Such clones will keep particular humans (donors for the clones) within the loop of responsible decision-making processes and, at the same time, make such human involvement ubiquitous and, therefore, efficient. ...
Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0 proclaims transition to digitalization and automation of processes while emerging Industry 5.0 emphasizes human centricity. This turn can be explained by unprecedented challenges being faced recently by societies, such as, global climate change, pandemics, hybrid and conventional warfare, refugee crises. Sustainable and resilient processes require humans to get back into the loop of organizational decision-making. In this paper, we argue that the most reasonable way to marry the two extremes of automation and value-based human-driven processes is to create an Industry 4.0 + Industry 5.0 hybrid, which inherits the most valuable features of both - efficiency of the Industry 4.0 processes and sustainability of the Industry 5.0 decisions. Digital cognitive clones twinning human decision-making behavior are represented as an enabling technology for the future hybrid and as an accelerator (as well as resilience enabler) of the convergence of the digital and human worlds.
... Dynamic refers to how a platform develops over time within its ecosystem by attracting users and adding new capabilities., while performance pertains to the ability of the platform to succeed in competition (Golovianko, Gryshko, Terziyan, & Tuunanen, 2022;Zhao, Von Delft, Morgan-Thomas, & Buck, 2020). ...