Researchers Discover Novel Algorithm for Universal Problem-Solving
What if we had a way to solve the world’s most challenging problems?
Instead of being filled with worry and anxiety, instead of getting frustrated while ruminating to solve impossible puzzles in our minds, what if we could just outsource this process to computers and have these powerful machines help us with it?
A recent publication describes a new database architecture that’s promising to help us balance the equation between each individual’s desired preferences and the systemic solutions that we can actually implement to make it happen in such a way that is fair and equitable for everyone.
In early 2022, researchers have published a proposal for a decentralized problem-solution directory that can be used for solving any, even unknown problems democratically with crowdsourcing.
That means it will be a place where anyone can easily publish and look up solutions for any problem, no matter how big and complex, or small and personalized.
The implications for our civilization and society are far reaching: What if instead of fighting, we could work together on making the world a better place for everyone?
In today’s world ther are various agents acting to protect their interests. No matter if it’s individual humans, state actors, or corporations, they all have unique motives and work to eliminate subjective obstacles in order to pursue their agendas.
The paper proposes a standardized user interface (UI) for a multi-dimensional node network. Building and populating the network with the UI translates into a step-by-step process that can be used to find solutions to any, even unknown problems. It’s a universal problem-solving algorithm.
In other words, when building a tree-like web with the root causes of each problem, the network becomes more intelligent and helps us find solutions to problems that we didn’t even know we had.
This method works for individuals acting alone or for groups of people developing solutions collaboratively.
There are three major improvements in this network compared to existing technologies. The leading innovation is to classify different types of information into distinct categories, as well as qualifying the connections between these types of information based on their cause.
The key differentiators are:
- Defining problems as negative descriptions of unfulfilled needs
- Separating actionable solutions from intangible value goals
- Modeling cause and effect of problems and solutions with connections
Most people would agree that humans are primarily driven to fulfill their own needs & desires, or help others achieve theirs for a mutual benefit. The things we want are our “value goals” (sometimes referred to as “intrinsic motivation” or “terminal goals”). This is often attributed to the famous Hierarchy of Needs, popularized by Abraham Maslow “Theory of Human Motivation” in the 1940s.
While Maslow’s Hierarchy remains controversial, Mental Contrasting with Implementation Intentions (MCII) has been shown effective for goal attainment in studies involving almost 16,000 individuals.
That means that focusing on our dreams and desires is useful (positive motivation) but it becomes more effective when we actually root these ideas in realistic expectations. When working towards achieving our goals we need to be aware of adversity, problems and obstacles that we might face along the way.
This may sound like generic self-help advice or pop psychology, however in the workplace as well as in business, sales, and marketing, these principles are applied every day to create innovative products and solutions for consumers.
And it is no secret that many industries are lobbying to politicians so that they can influence legislation to give consumers easier access to their products and services.
However, this is exactly where we see so many systemic problems arising, and recent years have shown us how difficult it is to separate the decisions of individuals from systemic impacts. No matter if it’s poorly trained police officers acting on their racial bias, or if it’s giant industries influencing public health policies. Our needs, wants, goals, dreams, values, and desires are all interwoven and mediated by the material actions we take, and by the systemic infastructure within which we take them.
This makes is so that our benign preferences and intentions create an ever widening gap between that which we really want, and the problems and negative consequences that the actions of others, and sometimes our own, have created.
Climate change and environmental issues are one great example of this. The industrialization in the 18th century brought much wealth and innovation. But also, coal plants created pollution and made people sick. Nowadays, electric cars are supposed to help prevent climate change, but the mining of lithium and other minerals is also destroying the very environment that these cars are supposed to protect.
For individuals it can be hard to make sense of this complex world, without feeling overwhelmed, frustrated or disempowered. Add to it, that prior studies have shown an assymetric hedonic contrast, meaning that pain and discomfort will be amplified over positive experiences. Although we appreciate wins, losses can feel quite aweful by comparison.
That’s why the researcher’s proposal is to create a “universal solution object” that will help us balance the equation by intelligently floating the best solutions to the top. The system makes sure that we can actually act upon viable solutions rather than being mislead by unrealistic aspirations.
Not only can we visualize which solutions have which positive effects, we can also avoid those solutions that have too many negative consequences.
Finally, in the network, the connections between entries form a cause and effect chain, because each entry in the network can have nested child or parent entries. As a result, the user interface offers a unique method for performing a recursive root cause analysis.
This way, when solving a problem, users can easily identify root causes, and focus on solving those with the same MCII process: Identifying the goals and actionable solutions for each root cause problem.
This creates more clarity and mutual understanding around problems, their negative consequences, and their causes.
The potential for this technology is promising.
Imagine what it would mean that we can say, with confidence, that we have a solution for every problem, or that we are at least on track to creating a solution for every problem.
It seems like there is a lot of potential to make the world a better place.
You can learn more about the technology and read the full white paper here.
