What is the difference between private knowledge, shared knowledge, and common knowledge in Game Theory?
Private Knowledge
Private knowledge refers to information that is exclusively held by an individual and not readily accessible or fully understandable by others. A prime example of this is consciousness and personal life experiences. These experiences are inherently private and even if we attempt to share them, the descriptions are often reductionist and fail to capture the full essence of the experience.
Shared Knowledge
Shared knowledge is information that is accessible to a specific group or potentially all individuals. Each person within the group can learn and understand this knowledge. A relevant example is the information available through ChatGPT, which is accessible to everyone. This type of knowledge is publicly available and can be disseminated and understood by multiple individuals.
Common Knowledge
Common knowledge builds upon shared knowledge by adding an additional layer of mutual awareness. It is not only shared but also includes the understanding that everyone knows the information, everyone knows that everyone else knows, and so on, ad infinitum. This recursive aspect of common knowledge is crucial in game theory as it profoundly influences decision-making processes within communities, organizations, and societies. Although this epistemological knowledge is often hidden from our immediate consciousness, it has a significant impact on collective behaviors and strategic interactions.
Practical samples
Economic
Money serves as a prime example of how common knowledge is essential for its value. While an individual might understand and trust a certain currency, the true worth of that currency hinges on the belief that others also know about it and respect its value. If someone thinks that nobody else recognizes or values the currency, they are unlikely to invest in it. This is because the effectiveness and worth of money rely on a shared understanding and mutual respect. Without common knowledge — where everyone is aware that others also acknowledge and respect the currency — it becomes worthless.
Traffic signs
Traffic signs are another excellent example of the necessity of common knowledge. Nobody would risk driving if they believed they were the only ones respecting traffic signs. The mutual understanding that all drivers recognize and obey these signs is crucial for road safety.
How can we know that others respect traffic signs? The existence of a driving license is a key indicator. It signifies that the driver has undergone training and testing, ensuring they understand and adhere to traffic regulations. This shared credential means that not only do they know the rules, but they also know that other licensed drivers know them as well. This creates a foundation of common knowledge, where each driver is confident that others will follow the traffic signs, ensuring a safer driving environment for everyone.
The Importance of Common Knowledge in Software Organizations
In software organizations, common knowledge is crucial for effective coordination and cooperation. Consider a scenario where one team member is working on a new issue. Simultaneously, a member from another team is also tackling the same issue, but they are unaware of each other’s efforts. This lack of awareness can lead to duplicated work and inefficiencies.
If they could know that the other knows about the issue, and understand that each knows the other is aware, this common knowledge would enable them to coordinate and collaborate more effectively. This epistemological knowledge facilitates better communication and cooperation, allowing for more efficient problem-solving and resource utilization across teams.
And this epistemological knowledge creates social norms. When one team member discovers something or solves an issue, they feel obliged to share their findings with the group that is working on the same problem.
Moving from general epistemological knowledge to social epistemological knowledge creates a paradigm shift. This shift transitions from dealing solely with general or work-related knowledge to addressing relationships with individuals or groups. It fosters an obligation to the well-being and values of the group.
From technical to social
As a result, a technical engineer engaged in technical research may find themselves navigating social norms and cultural expectations. When they share their discoveries with the public and make their presence known to others, they not only contribute to the technical knowledge but also engage with the social dynamics of their group. This integration of social epistemology emphasizes the importance of collaboration, mutual respect, and adherence to group values, extending the engineer’s role beyond mere technical contributions to include fostering a positive and cohesive team environment.
The Limitation of AI in Facilitating Common Knowledge
Common Place to Acquire Knowledge
AI tools like Chat-GPT and its competitors provide a common ground for knowledge acquisition by encompassing many articles and sites from the internet. These platforms offer a centralized source for a wide range of information, making it easier for individuals to access and learn from a vast pool of knowledge.
Shared Knowledge
These AI platforms also facilitate shared knowledge. Since anyone can access the information that Chat-GPT holds, it ensures that knowledge is not restricted to a single person or group. This widespread accessibility helps in disseminating information broadly, ensuring that many people can learn and benefit from it.
Learning Using Personalized Definitions
AI tools assist in making knowledge more accessible by explaining concepts in various ways and answering questions tailored to individual needs. This personalized approach helps users understand and learn complex topics more effectively, catering to different learning styles and requirements.
What It Does Not Provide — Common Knowledge
However, these AI tools fall short in creating common knowledge. If you are currently investigating a topic, you cannot know who else is also researching it, even if those individuals are open to collaboration. This lack of awareness keeps individuals isolated and prevents the formation of epistemological knowledge about who else is working on the same issues. Consequently, AI tools do not provide the common knowledge necessary for effective collaboration.
