Does the Algorithm Heal a Company Organization?

Does the Algorithm Heal a Company Organization?

Martini Luca
DOI: 10.4018/978-1-7998-8884-0.ch017
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Web-based systems have entered the school and business environment widely and globally. Simultaneously, instant connections such as those enabled by twitter in everyday life have bled into the workplace, calling for a relational multimedia platform with a universal interface to facilitate knowledge flows, with cross-pollination in terms of “knowing that,” “knowing how,” and “knowing why.” The use of PEGs is interwoven throughout the processes related to the introduction of relational multimedia platforms into organizations—pre-implementation, during implementation, during on-job training, and in the process of work itself—helping develop the coherence necessary for successful and sustainable business.
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Karl Marx argued that services were unable to produce capital (Marx, 1980) but the author believes that this idea is no longer valid as a result of the technological revolution, which, as Michael Dertouzos, then Director of the MIT Laboratory for Computer Science, argued is unfinished. Technology offers new promises in order to create a collective awareness of the business organization, which comes precisely from the providers of intellectual services accumulating in the company database, with the value of individual experience expanded through the possibility of its rapid reproduction in liquid form (Dertouzos, 1997, 2002).

Thanks to the new computer algorithms in PEGs (Psychecology Games) that synthesize and apply Jungian theories (Jung, 1933) to the healing of the WEB (Schafer, 2020a, 2020b, 2016), it has become possible to concretely implement new systems of accumulation and reproduction of the intellectual capital of organizations by bringing together individual know-how, and, above all, by helping resolve emerging issues across components of the organization and helping to develop indispensable emerging values such as integrity, empathy, transparency, participation, collaboration, learning and creativity (Bennet et al., 2018; Andreason, 2005).

All this creates a new indispensable humanistic and philosophical promise in which, hand in hand, human and artificial intelligence will be able to march towards a new era of social progress, united and interconnected through a WEB platform that has finally healed. An example of this type of platform is “Ysarmute”, which will be used to explore the software approach needed to support healing.

The Ysarmute algorithm is an original system consisting of a new communication metaphor that moves beyond historic desktop approaches in managing communication among the individuals that, with their intellectual abilities and experience, ensure the company creates centralization units of their thoughts and experience to produce long-term profits. This new algorithm, condensates in its database the reconstruction of collective knowledge, which until brought into the system is thought of as individual knowledge, making it possible for a company to accumulate and remunerate a new type of multimedia intellectual capital building on the collaboration of individual brilliant minds inside the organization. While this may sound like what many Knowledge Management systems have promised in the past (Dalkir, 2017; Milton & Lambe, 2020; Shekar, 2021), this moves far beyond the summarizing and linking that occurs in historical expertise locators and systems, or in systems designed to link conceptual models and patterns (higher mental thinking) to logic-based concrete examples (lower mental thinking) (Bennet et al., 2020).

The Relational Multimedia Platform is primary to the Ysarmute algorithm, which is created as a digital-relational tool designed to help achieve the organization of individual expertise inside a company, in the expansion of working capacity skills (individual productivity) and in the relative remuneration, no longer on an hourly basis, but by objectives. The human and relational characteristics required by these pioneers of collective knowledge accumulation are not frequently traceable. They have to do with the ability to account for one’s own psychic flow at the basis of their experiences and spheres of competence, in order to make their value accessible to third parties. As has been extensively shown in the past, this is not an easy task (Rohman et al., 2020; Vihersaari, 2015). Unity, sharing and helping (on-job training) between each other inside an organization, through a new user interface metaphor, makes visible potential negative traits such as jealousies and selfishness, which might be typical to an individual nature yet simultaneously devalue an organization’s intellectual capital.

When introducing the Ysarmute algorithm, individuals in management positions are asked a series of individual risk factors to be managed as drivers of collective knowledge, its accumulation and remuneration. A Personal Event Technique (Martini, 2020), which is detailed later in this chapter, helps to identify and manage these risk factors by putting the person at the centre of their analysis while working and, by doing so, creating the indispensable conditions to introduce the algorithm in companies with a shortcut to resonance and coherence (Bennet, 2022).

Key Terms in this Chapter

Knowledge Artifacts: Information that has been successfully used in the past and offers potential learning for future decision-makers. Note that since knowledge is context-sensitive and situation dependent, “knowledge” successfully used in the past is not necessarily “effective” in current and future situations, and thus is considered a “knowledge artifact.”

On-Job Training: A self-training approach which pairs the teacher and learning, with the teacher beside the individual employee studying his work processes in order to help him identify behaviors he would not use and which behaviors would save him time if he used them , teaching while he works. This approach is tailored to organizational needs. For example, in order for an organisation to promote increased decision power in its people, it has to provide them with more knowledge about why some things are done and who has done them, and why a certain decision is made and who has made it (Dertouzos, 1997 p. 212).

Universal Interface: An interface created at the experimental level which is “valid for making every type of work performance” (Carinci, 2020). The new algorithm takes an objective approach, accumulating in the database of the company both the basic data and information, the intellectual contribution of employees, and the experience used by each person in determining along with their know-how (the knowledge, for example, of how to build a computer from A-Z), customer satisfaction, customer loyalty and their remuneration, measuring this contribution in proportion to the money collected from customers, the time dedicated to problems, and communication of the information from which relative solutions were obtained (Martini, 2021). With this interface, every worker has the awareness of having to communicate with other colleagues the factors that have allowed them to reduce the risk of error in their duties, also indicating the supports and logic used to arrive at the definition and solution to problems in the relationship with the customer. This all comes back to communication between the individual centralization units of each specific knowledge managed within the company to create the product or service to which different knowledge contributes, or to which a specialized knowledge contributes.

PEGs: Abbreviation for Psychecology Video Games. PEGs serve as analogs based on a source code that depicts all known EM dynamics (algorithms) that contribute to real-life conscious/unconscious humanly embodied dynamics. Symbolic Languages contain a narrative–metaphorical common denominator that may serve as a transducer among diverse linguistic contexts that are used to understand the emergent figurative reality of psyche-physics. The PEG template may be thought of as an artificial neural network like a Kohonen map in-order to correlate recursive input-output self-organizing map lattices. The self-organizing map is a single layer feedforward network where the output syntaxes are arranged in low dimensional (usually 2D or 3D) grid. Each input is connected to all output neurons. Attached to every neuron there is a weight vector with the same dimensionality as the input vectors. The number of input dimensions is usually a lot higher than the output grid dimensions. SOMs (self-organizing maps) are the most popular neural network models. In the category of competitive learning networks, SOMs are models used for unsupervised learning. This means that no human intervention is needed during the learning and that little needs to be known about the characteristics of the input data. “We could, for example, use the SOM for clustering data (such as contextual personality and story premise) without knowing the class memberships of the input data. The SOM can be used to detect features inherent to the (story) problem (or premise) and thus has also been called SOFM, the Self-Organizing Feature Map. SOMs are mainly used for dimensionality reduction rather than expansion (Hollmen, 1996). The goal of the learning in the self-organizing map is to associate different parts of the SOM lattice to respond similarly to certain input patterns. This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain (Simon Haykin, n.d.).

Personal Event Technique: A technique to make the company's staff involved and at the same time accompanied in the co-design / planning and “responsible” management of the communication system so that its final structure is aligned with the objective of producing profit in a lasting way through the management of factors of individual risk consonant with the job actually performed by the person. The technique—designed to manage any type of communication—organizes tasks by objectives, verifying whether the risk factors indicate in detail the points that need attention in order to achieve the various purposes assigned by the management. The use of this technique in setting up a relationship with the worker is very fruitful, and is the preliminary stage for implementing smart working in a truly productive way for the company (Martini 2021).

Knowledge: Expanding on “Justified True Belief”, the definition of knowledge credited to Plato and his dialogues (Fine, 2003), knowledge is considered the potential and actual capacity to take effective action (Bennet et al., 2018). This capacity is built on “knowing that ” (knowledge informing—the information or content part of knowledge) and “knowing how ” (knowledge proceeding—the process and action part of knowledge) (Ryle, 1949; Bennet et al., 2018)—as well as “knowing why ”, which is related to “First Cause” intention (Falcon, 2019), a critical aspect in PEGs—so users can clarify and share their “ illuminative strand ”, seeing the truth through the eye of the mind (Wilber, 1983).

Relational Multimedia Platform: A technology platform that allows the use of knowledge artifacts, that is, information that has been successfully used in the past and offers potential learning for future decision-makers. This platform supports the working method of an organization based on the contribution of each of its users in the formation of the database. It is “multimedia” because everyone’s contribution can be documented with files in various formats. It is “relational” because as it instantly stores communications it is the result and proof of all the experiences, connections and relationships among workers that are necessary to face every situation to date that has occurred in the organization. The RMP instantly stores everyone’s contribution in the form of a “hyperfile” (Dertouzos, 2002 p. 108 – 109). The crucial elements are the tools (Universal Interface and Ysarmute algorithm) to quickly record and document our thoughts on the things we are doing at the same time as we are doing them.

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