Organizational Evaluation Perspective of the Intelligent Consortium: Intelligence Degree

When observing traditional organizations, people often evaluate their quality based on historical performance. However, an organization’s historical performance is influenced by multiple factors. For example, a company may achieve impressive results due to a booming industry, but this does not necessarily mean it deserves high praise from an internal operational perspective. People may also evaluate an organization based on the state of its managers. For instance, when encountering a charismatic and eloquent manager, observers may naturally give the organization higher praise. If the organization behind such a manager also has an impressive historical performance, evaluations may be filled with admiration and acclaim. In my professional experience, I often discuss investment firms and their managed funds with friends in the financial industry. I’ve found that most people’s evaluations of fund companies and their products are heavily influenced by recent historical performance, as well as the PPT design skill, presentation content, the spokespersons’ performance, and even the spokespersons’ attire. Thus, their evaluations are often swayed by the “hot hand” effect of fund historical performance, with few analyzing the company’s organizational structure, decision-making mechanisms, or execution mechanisms in depth. Even when mentioned, such topics are usually brought up casually without further exploration.

However, an objective organizational evaluation should move beyond the influence of historical performance and managers, focusing purely on analyzing and judging the organizational structure and operational mechanisms. Only in this way can the quality of an organization’s core be objectively assessed.

In evaluating the Intelligent Consortium (organization), the intelligence degree should be a key perspective for observation and evaluation. When assessing computer neural network intelligent agents, we similarly evaluate internal structural characteristics such as neuron layout, activation function choices, the number of convolutional layers, and dropout structures. While training results and practical application effects remain important considerations, they are influenced by datasets and cannot fully reflect the model’s intelligence potential. The internal structural characteristics, as the foundation of the model’s intelligence degree, determine the intelligent agent’s intelligence potential. For example, a model with only 50 neurons and two network layers may suffice for simple tasks, such as recognizing digits from 0 to 9. Still, it would inevitably fail at complex tasks, such as face recognition (even with sufficiently high-quality training images), as such a simple model lacks the potential intelligence required for complex tasks. Under the Intelligenism framework, organizations are viewed as intelligent agents. When organizations are constructed based on intelligent agent architecture (the Intelligent Consortium), we can similarly develop and apply an analytical framework akin to neural network analysis to evaluate the organization’s intelligence potential and intelligence degree.