Knowledge Models

This is where we design and build our knowledge models, our ontologies and our patterns of meaning across all domains. Help us identify the variables and relationships that will come alive through models. Initially, we focus on the highest levels of knowledge representation and less on levels of detail. We organize first by using the words we are all familiar with, and then translating and expanding these linear placeholders into rich dynamic maps of relationship, exchange and complex knowledge.

We believe initiative, creativity, thoughtfulness and passion for the architecture of science and natural systems will allow us to construct dynamic models and large scale representations of everything complex.

We believe everything is a component of multiple interconnected systems and events.  Given the technology at our disposal, we think the best way to organize and display this complexity is through pre-built iconic models populated by ontological categories organized visually for easy identification. We deliver meaning through an elegant array of colors, flow, and pattern that propagate on top of and within the models. Here are some of the principles we are using to guide the development of knowledge models:

  • Everything has programming at its core and is dynamic
  • Knowledge is comprehensive systemic understanding
  • Y Worlds communicates knowledge by generating a spectrum of patterns representing past, present and future while addressing the objectives of a user
  • Data is are points of association that contribute to understanding.
  • Ontologies are useful schemas of organization. Technology allows us to develop different ontological frameworks serving different viewpoints and objectives. We will craft ontologies into visual models that operate within map windows
  • We are building a visual language.  We think assigning meaning to certain colors, shapes and forms makes sense.

Here are our current models:


yworlds-models
slantobjectivesco-opmistakesdegrees-separationnewsthreatspolarityclimatewindpublic-policy-iconharmonics

 

We look forward to the exchange of ideas about how to construct ontologies and transform them into living representational visual models on screen.


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7 thoughts on “Knowledge Models

  1. This is a concise set of variables that can be arranged and combined to fluently describe a tremendous range of community/network activity. If each end user can at least minimally affect the status of a few given variables in conjunction with the greater network, the community will gain the capacity to optimize relationships among users, information, and technology. The cumulative influence applied by each end user making discrete adjustments on his or her own virtual dashboard will generate the type of nuanced and timely aggregate visual feedback that informs and drives future decision making. Thank you for laying out this new approach to interactive learning and development!

  2. Thanks Tom. Coming very soon is our first visualization engine that will allow us to begin to make the models come alive with pattern and movement and meaning. It is hard to imagine how these models work until they work. Once we begin to have access to gaming-like toolsets to build our models, all of our viewers will be able to create and experiment and begin to build a new systemic language.

  3. It seems clear that getting the data and the algorithms flowing a.s.a.p. is critical. The sooner “we the people” start engaging with dynamic models such as this, the sooner we can start to grasp just what is possible. A whole new realm of intelligence is within the grasp of digitally networked communities.

  4. Hi Alan, I am having a hard time discerning what exactly is the specific technology you have developed, and which you are NOW bringing to market. I hear a lot of very attractive Future ambitions, notions and sentiments expressed in your material, but I can’t truly see more than this AT THIS Time. Can you help me out here? Is there more to this than what I have noted above? Thanks!

  5. Also what is the theoretical basis you are using to design the shapes and colors shown above. What is the specific thinking that underlies all this? Thanks!

  6. Hi Marcus. Good to hear from you. I invite you and everyone to skype with me to discuss Y Worlds. I am at alan.yelsey1 on skype, or direct email alan@yworlds.com. Here are some quick answers to you questions. For specific technology, we have just put out a visualization engine and will publicly release it after a quick beta. We will send you the beta. But with that said, our vision of our technology requires more people and money to realize it. Simply, we believe a video game like real time browser based engine to build representational worlds and models is the way to go. We are about to bring our prospectus to potential funders shortly. The big idea to realize is that browsing, knowledge and meaning can be be conveyed by a semiotic language complete with cellular models filled with specific colors and patterns that convey meaning among the variables. Our new engine is a small first step toward realizing this vision. The theoretical based for shapes and colors is cognitive research but…we totally believe in collaborative development and expect and desire many people to iterate our starting points, improving them and adapting them and replacing them with something better. The basis for the colors and shapes and overall template for a new visual language for complexity and knowledge is that humans process small groups of constructs, and get overwhelmed by 30 variables with lots of lines. So we choose bite size ontological models (robust and flexible) that have a small number of shapes (simply rounded conveys living, vertical rectangles are man made etc.) and our colors are composed of just 5 for scale (good to bad) and just 4 for validity. The action is the pattern and flow of the colors within and among the cells of the models. The simple mechanism we suggest can depict complexity instantly at every level and perspective view. Our research and followers indicate we are on the right track with this approach, but cannot fully realize it because the code to build this is still emerging, and quite cutting edge. Alan

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