**1. Introduction**

The state-of-the-art management literature focuses on the qualitative characteristics of management, bringing empirical evidence-based models for improving organization performance. However, the management models that appear in the literature do not consider the individual complexity of organizations, thus limiting the reproducibility of good results. The organization digital twin (ODT) used in the article demonstrates the potential of RL-AI to analyze and quantify complex phenomena related to organizational behavior. In this article we study model-driven reinforcement learning AI as a new method in improving organization performance at complex environment.

There are two main categories of artificial intelligence (AI): data-driven and model-driven. Data-driven AI uses data in finding correlations and forecasting the future. In model-driven AI there is model that simulates the environment. Reinforcement learning (RL) focus is in learning and finding behavior which gives best value and reward. When RL is utilized at model-driven AI the model simulates the behavior's effect in the value. The agent tries to learn the best behavior by following the model's reward signals. Thus, the behavior of the agent determines the result, not the data of the past.

Game Theory is a branch of mathematics that are used to model the strategic interaction between different players on a context of predefined environment. At management game theory there is predefined organization environment where the players are leaders and team members as workers or employees. Each player has incentives that drives their behavior in the game. Management game is nonsymmetric because leader has specific and non-changeable characteristic compared to workers. Workers are motivated in maintaining and improving their work performance and personal self-esteem. Team leaders are motivated in maintaining and improving team performance, which is related to team leader personal profit incentives. Team leader knows that team performance is essential for achieving team profit targets. Workers know that their personal incentives will improve if their work performance is good. Thus, if there are problems at work the rational policy would be to tell the problems to supervisor so that problems can be solved. In addition, solving problems may improve workers self-esteem, having hidden psychological incentive. This organization environment form state space for strategic-Bayesian-stochastic-nonsymmetric-signaling game.

Nash equilibrium is a concept of game theory where optimal outcome is the balance where all players incentives are considered and fulfilled in optimal way. If team leader gives positive feedback for raising the possible problems, it will have positive effect on workers' self-esteem, fostering workers policy to inform the problems by signals. Solving the problems will improve group performance which foster leader's policy to encourage workers signaling game. This way workers and supervisor may find equilibrium of policies (strategies) which lead to general-sum game where optimal and sustainable team profit performance is achieved. However, this article explains why this optimal equilibrium is difficult to achieve in reality. Bersin [1] study reveal that 89% of managers think that leadership is important issue, but current leadership programs bring only minor value in improving leadership quality. This article argues that modern reinforcement learning artificial intelligence gives one solution in solving leadership challenge.

In addition to administrative role, the HR management has important function on adding competitive business value to an organization management (for example see references [2–6]). Managers need predictive measurements that indicate how business is developing and how to improve it. Human assets are essential for creating competitive advantages, thus interest in performance management has increased. Fleetwood and Hesketh [7] argue that researchers should better understand the complexity of the organization environment and seek to open a "black box" of causal relationships between human resources and organizational performance rather than offering simplified solutions.

Several studies indicate that employee psychological well-being has tendency to predict business value of an organization (for example see references [2, 8]). However, management can be confused of how to improve well-being and how much effort should be invested in well-being development at different situations to gain sufficient payback. Research reveals that organizations expect artificial Intelligence to help reducing managerial biases related to human issues and to improve productivity and employee experience [9]. Beside the hopes, researchers are also concerned that artificial intelligence may cause serious harm if the organization context is oversimplified by using data driven machine learning algorithms [10]. This article argues that AI can help solving difficult management problems

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**Figure 1.**

*Concept of organization digital twin.*

*The Digital Twin of an Organization by Utilizing Reinforcing Deep Learning*

to be explored and how accurate do the predictions need to be" [11].

recognized before any reliable simulation analytics can be made.

simulate the reality the digital twin must meet following requirements:

• The environment state can be verified from measuring the reality.

**2. Concept of organization digital twin**

related to human biases. One of the most promising new technology is Digital Twin that uses simulation model driven AI. "To build an efficacious Digital Twin, it's important to first agree what problem needs to be solved or what opportunity needs

Human competencies, for example leadership and working skills, have certain causalities to long term productivity. It seems that human competence has three performance-driving characteristics that can be described according to motivation theory as feelings of safety, team culture, and passion for work. It is clear that a passion for work affects a person's performance in a very different way than for example occupational safety issues. In addition, human is a psychophysical entity tied to his own situation. Therefore, the combination of all motivational drivers

First, we have to study human capital productivity, which includes working time

and the utilization of intangible human assets. Human intangible assets refer to performance on how effectively is the working time utilized, and how much value a person produces at each working hour. An employee may work for eight hours a day, but out of that working time, how much is actually used effectively in creating value? This basic understanding of how each employee produces value needs to be

At this article the organization digital twin (ODT) refers to the mathematical environment that simulate organization human capital productivity. To be able to

• Markov property: The future is independent of the past given the current

*DOI: http://dx.doi.org/10.5772/intechopen.96168*

determines performance [12].

situation.
