Infovation: Working with Information Part 3 – Understanding Information

Understanding

The next task (No. 2) in infovation lies in working with the collected information, that is, interpreting data to understand the essence of the object under study.

All work in infovation has a primary goal: to make sense of information that reflects certain events. This means understanding whether something happened or could have happened, what information is missing, which data is clearly falsified, and so on.

Collecting and selecting information is similar to investigative work—a kind of detective’s path following the traces of the “studied” object. When enough traces are gathered, they must be interpreted, i.e., understood. Interpretation is often the most interesting stage of research. At this stage, infovation highlights three main steps in the information processing algorithm:

1. Mental experiments with collected information: grouping, mapping, chronological and synchronic comparisons, systematic combination, “translating” data from one type to another, quantitative and qualitative analyses, modeling, and more.
2. Forming the conceptual field of the research: considering systemic relationships of research objects with territory, time, existing technologies, societal development, civilizational processes, psychological characteristics, and more.
3. Developing probable scenarios of events (processes, phenomena) involving certain actors: modeling possible developments considering cause-and-effect relationships and systemic connections, testing and breaking down versions, and creating counter-versions.

All cognitive experiments are closely linked to human reasoning. They are, in essence, a “fusion” of a person’s paradigmatic views on what is possible or impossible in light of known data. People perceive information according to their education (or lack thereof). The same information can be understood differently by different people, which may or may not lead to discoveries. Mental experiments with information are primarily intended to develop the skill of seeing something new in what is already known.

They also help form the habit of detailing and clarifying data, rechecking various combinations to find the most likely explanation for changes in the studied object. All mental experiments are, above all, about developing the habit of thinking, not just consuming a cocktail of fragmented information. The result of cognitive experiments may even be the rejection of previous versions or the realization of their incompleteness. This kind of mental work also allows for more adequate answers to the question “WHY”—to judge what was, could have been, could not have been, or is unknown.

Essentially, it’s the ability to see the new in the familiar. In other words, to shift one’s usual paradigm when necessary. Managing one’s perception is a crucial skill, especially in a world built on manipulation and suggestion.

The toolkit for mental experiments is, in principle, inexhaustible. However, there is a rule: start with simple and accessible mental modeling. The simplest operations are thematic, chronological (including synchronic), and cartographic groupings of information. These basic operations are often skipped, which naturally reduces the quality of research from the very beginning. Any quantitative, statistical studies, “translations,” etc., make sense only with carefully classified material. Otherwise, errors are embedded from the start. A productive method is to build mental schemes of existing information about the object, allowing for additions and clarifications. TRIZ (Theory of Inventive Problem Solving) approaches can also yield interesting results when working with information.

It’s important to realize that any interpretation remains just that—an interpretation, not the actual event. Understanding material is a highly subjective process. An interpretation based on a good understanding of contextual events can be more realistic and probable than one based on poorly collected material.

The conceptual field of the studied object is not only a body of connected information but also data for systematic verification. In any system, there are objects and connections between them. Information is, above all, data about relationships between objects. The conceptual field is essentially a kind of thesaurus—a treasure trove of knowledge on related topics that can connect, supplement, or even destroy certain ideas. The broader the researcher’s outlook, the more fully they will see potentially possible or impossible connections between objects. A well-developed conceptual field allows for modeling studied objects at a qualitatively new level of understanding and serves as a basis for potential systematic research.

Interpretation of studied events is recorded as probable scenarios. A scenario implies the presence of significant base information, collected and analyzed, presented as a coherent narrative. This narrative should allow understanding of the essence of the events, have internal logic, demonstrate cause-and-effect relationships, and show the overall system of events. Research topics can generate a variety of possible scenarios due to both the excess and lack of information and the difficulty of selecting truly significant elements. Also, it is often impossible to fully convey the meaning of one’s findings. No matter how hard the researcher tries, some information used in the interpretation will remain only in their understanding and outside the scope of transmission. The main tasks at this stage of working with information are:

• Developing the habit of reasoning based on data, not just memory or belief;
• Acquiring divergent thinking skills, allowing comfort with multiple possible correct answers;
• Mastering systematic work with information;
• Being able to create probable scenarios of processes based on detailed and systematized information.

This stage of research is reminiscent of “reproducing a hologram”: the more accurately possible perspectives are recorded, the more accurately the object can be reproduced (and reconstructed). The similarity between holograms and the brain’s distributed, associative information processing makes this metaphor especially interesting and worth exploring for new analogies and meanings.

This stage can also be the final stage of research. A person figures out a question, reconstructs the scenario, records the results, and moves on. But usually, that’s not the case. Often, there’s a desire to discuss findings, explore possibilities, or, even better, share the collected information in a digestible, interesting, and well-argued form.

Mental Experiments with Information

The use of research tools is not dogmatic but rather a potential “set” of actions for the inquiring mind. For example, grouping facts allows you to:

• “Collapse” and “expand” information,
• Reevaluate the existing perception,
• Clarify details,
• Form additional probable scenarios.

“Translating” data from one type to another aims to present data differently. For example, text presented as a diagram is perceived differently and gives a new understanding of the relationships between measurement parameters.

Cause-and-effect matrices and networks allow you to create blocks of connected information, revealing and organizing the object’s connections with other phenomena. Similar and complementary tools include matrices of interpersonal relationships, which help visualize relationships between people, groups, and organizations.

Mental maps help organize information into simple, understandable models with visualized connections.

Context analysis allows you to “embed” the studied object into its situational environment and assess inconsistencies and contradictions that arise during research. This analysis requires evaluating a wide range of available data (from linguistic to geographic and chronological). Various types of synchronic tables can also be productive in context analysis.

Technological analysis allows you to assess the technology of making something, as well as reconstruct technologies for movement, extraction, or disposal of resources. Presenting this data as “translations” (route sheets, organizational mental schemes, block diagrams of technological processes, etc.) allows for more accurate reconstruction of presumed technological processes. These processes, when passed through a contextual synchronic table, can reveal inconsistencies in chronology and technological capabilities of the era.

The infovation toolkit does not prescribe dogmas. It offers a set of simple, understandable actions to improve the quality of information preparation and processing.

The choice and order of tools are strictly individual and depend primarily on the researcher’s thought process. The entire research toolkit is an open, flexible system. The researcher can perform operations with the studied information in any sequence that makes sense to them. The only requirement for all transformations with information sets is the presence of high-quality primary information (and regular recording of intermediate research results, which will later participate in forming the research data set).

“Translation”

The main task of “translation” is to see familiar information differently.

For example, it can be productive to “translate” data into another form: digital data (the question “HOW MANY”) can be visualized with diagrams, providing a new perception of the data. Translating information into a “question field” can highlight entirely different characteristics of possible event developments.

For instance, the technological path of producing an object (the question “HOW”) can be presented as a block diagram of the production process, allowing for an assessment of the required resources and raising questions about the feasibility of such a process. Notably, the question “HOW, IN WHAT WAY” is often ignored in research. A coherent explanation of an event may be constructed, but it may not be technologically feasible (at least within known physical realities). Seeking answers to this question can serve as a tool for testing the quality of conclusions.

Aggregating information about an object’s logistics (the question “WHERE”) can be presented as a route sheet for the movement of, for example, building materials or cavalry. A visual “path” taken by the object can prompt new verification questions: are such movements realistic, what technologies would have been necessary? Questions may arise from material that has been structurally integrated into the research system.

Temporal relationships (the question “WHEN”), presented, for example, as synchronic tables, may suddenly reveal that Adam Smith was developing his theory of the market—still referenced by economists today—while at the same time, the Inquisition was actively burning witches across Europe. Or that there were a billion people on Earth, but in the 1800 U.S. census, there were only 5,308,483 people, of whom 893,602 were slaves. Is this possible? Or did some time shifts make their way into the documents? Again, these are all potential reasons to try to understand how events could have actually unfolded, whether there is any dynamic or correlation between population growth and external events, and what most clearly shapes these dynamics.

Such clarifications allow you to look at a known fact from a new perspective, which in turn helps break the automatism of perception.

Example of Data Translation

Information Analysis

Main tasks of the algorithm:

• Analyzing the information array about the studied object in various ways.
• Searching for matches in the system of data connections about the object.
• Placing the object in its event and technological context.
• Analyzing movements (routes) and logistics related to the object.
• Presenting information in a special way.

Fact Grouping

Fact grouping involves combining information within specified search characteristics: place, time, technology, logistics, people, author, etc., to identify potential connections. For example, creating chronological and thematic timelines allows you to present known information differently and group it in a unique way.

A chronological research timeline can be presented as a block diagram of key moments:

This kind of information presentation helps to see “extra” or missing elements, shows the time steps of changes, highlights periods of activity, or shows moments of passive process flow. Essentially, this is the work of creating a chronological sequence of events. Information about the research object may change over time, remain unchanged, disappear, reappear, or be reinterpreted. The researcher will always have a limited set of information. The ability to assess the dynamics of changes in information about the object also allows you to ask: how did it happen that, for example, a famous figure’s biography lacks a description of 30 years of their life? What happened during that time? Why are there no “traces” of their activity?

Grouping information by territorial ties allows you to clearly see where events occurred, which territories were involved, and how the trajectory of the research object’s movement unfolded over time.

Thoughtful work with maps allows you to assess the probability of events and, in some cases, track the locations of document sources and the routes of information about the object. This can lead to new research questions.

Additionally, maps can raise questions about toponymy, the evolution of names, spellings, and more. For example, the new type of Atlas by A. Ortelius (1570) gives country names that are surprisingly similar to Russian pronunciation and fundamentally different from modern names in foreign languages. This is a reason to study the evolution of names and find “transition points” from one name to another, compare these points across countries, and see what patterns may emerge.

After grouping data, results can be presented in various forms: block diagrams, graphs, mental maps—whatever is most convenient for the researcher. While a specific result cannot be guaranteed, it is certain that the person will see events differently, offering a new perspective on familiar facts.

All these schemes are also examples of “translating” data into new forms of information presentation. Each researcher may have their own set of schemes, tables, and models that are most comfortable for them.

This type of research work develops a kind of contour thinking—thinking that moves in loops, periodically returning to the original research question but at a qualitatively new level.

Grouping elements by territory, time, and theme allows you to assess the researcher’s level of awareness about the object. Often, judgments about a fact are superficial, but the researcher doesn’t notice this due to automatically filling in missing information.

An additional bonus of such research can be synchronic tables. By synchronizing temporal and thematic clusters of information, you can see the research object in potential relationships with its era, people, and scientific discoveries. These tables allow you to look at facts, not just familiar myths.

Combining Facts

Combining information allows you to merge elements from different sources, periods, and thematic areas, working with complex data about the research object. Essentially, combining is a cognitive experiment aimed at revealing possible meaning. The simplest way to practice combining is to learn to formulate “triangular” questions, which provide unlimited opportunities for combinations.

Combining allows you to “embed” verification information about the technological capabilities of the studied period, logistics schemes, biographical data of people connected to the object, and more.

Combining information into complex mental constructs can be done by merging objective and subjective data. For example, you can create a matrix for objectifying data about the research object:

The analysis of the research object develops according to a clear scheme, considering both objective and subjective data. Information is marked by clusters tied to research questions. Variations in forming information cells are possible, but the research sequence remains within the general scheme. The object has objective and subjective characteristics, which are collected and described in a way understandable to the researcher. Information is tied primarily to the place and time of the event and focuses on both the physical features of the object and its historical context, creating a field for data verification.

Analyzing the information source as a carrier of necessary specific data about an event, phenomenon, or person can also be interesting. Clarifying research on the reliability of the source can improve understanding of the material. For example, analyzing the source and recognizing it as unreliable can destroy the researcher’s entire argument. On the other hand, source analysis allows you to “reconstruct” the emergence of an idea and its introduction into circulation through replication. For example, Stephen Cummings, in his book “Reconstructing Strategy” (chapter 3), analyzes sources and the flow of an idea through different publications to propagate the notion of a special type of management. He shows that it wasn’t the idea of “effective management” that was good, but rather the mechanism for creating the impression of importance and thoroughness.

Don’t worry about the laboriousness of the process: it’s impossible to research material well without detailing. Every event results from countless facts and, therefore, countless representations of those facts. The researcher deals with a cause-and-effect network filled with “modules of connected information.” The researcher is always solving the canonical “needle in a haystack” problem—searching for what they don’t know.

Well-known business tools for combining include relationship diagrams, affinity diagrams, decision diagrams, matrices, and more, mostly based on mathematical approaches. Another interesting way to work with information is through an event connectivity matrix, which allows you to assess the involvement of information about studied objects in the overall information context of an enterprise. The choice of tools may depend on the researcher’s prior skills. The only condition: careful preparation of input information, assessment of its probable reliability, and evaluation of the source.

Mental Maps

An interesting research tool is presenting information as a mental map. A mental map on a single page (visible at once) allows you to create a kind of “sketch” of the perception of the studied event. This sketch activates imagination and clarifies the overall research outline.

For example, a mental map of the research process for a historical event N can show possible points of forced stops (collecting and marking information), looping (returning with new facts to a previous stage), branching into additional sub-studies, breaking down versions, and more. Each researcher may have their own research path, but they should see and understand it. A graphical representation usually provides a clearer understanding of the connections and features discovered in a particular study. Every mental model is unique. Its logic is universal: to clearly and concisely convey the essence of actions, for example, during research.

When developing mental models for deconstructing/reconstructing historical phenomena, a wide variety of information is usually combined: cultural, ethnic, technological, object-specific, and more. The mental model allows you to visualize the process of creating the image of the studied object and get closer to understanding its essence. The ability to combine facts is a truly genius skill of the inquiring mind.

There are many methods of information analysis: quantitative, qualitative, and technical analysis. All methods are good in their own way. To apply a particular research method and, in essence, “polish” information at some stage, you first need to collect high-quality information for this “polishing.” These methods are relevant only if the researcher operates with highly reliable information. Working with information makes sense only if the input data is carefully selected and verified.

Example (historical context)

Example (management context)

Conceptual Field

The conceptual field is formed over time. It’s impossible to know and fully understand information at the moment of first acquaintance. As Eduard Meyer wrote: “…the picture of events changes every time we receive new material, even where we already have extensive information.”

People often don’t think about the amazing nature of information when working with it, and thus don’t pay enough attention to important aspects: the quantity and quality of information, and the time spent on significant procedures for collecting and understanding information.

One of the earliest descriptions of this phenomenon is Bayes’ theorem, published in 1763. Reverend Bayes presented his conclusions as the theorem:

p(A|X) = p(X|A) p(A) / p(X)

where (A) is an event we want to know about, and (X) is an observation that gives us some information about (A). Bayes’ theorem provides a mathematical representation of the process of increasing knowledge about (A) in light of new information (X). The more new data (X) we learn, the more multifaceted our understanding of (A) becomes. Thus, Bayes’ theorem helps us understand how much our knowledge of A increases with new information X, i.e., how much the uncertainty about A changes as it is studied.

Research on the human brain shows that our worldview is redrawn every time we receive new knowledge. Thus, the process of cognition (and life itself) is dynamic. Most people don’t notice this due to the compensatory work of paradigms, which constantly perform a “stabilizing selection” of acceptable facts for subsequent awareness. It’s important to note that a brain unaccustomed to learning tends to “remember” information. It may see something new but will recall the old and quickly stop any cognitive effort. Thus, there are two modes of information perception:

• Cognitive (dynamically perceiving changes in the world; probabilistic, questioning);
• Remembering (statically perceiving the world as originally given; orderly, confident).

So, in a person’s mind live worlds of information, filters, images, and illusions. The mechanism of this coexistence is not fully understood. In the first mode, a person can accumulate new information; in the second, they usually collect “confirming” facts.

Example of a conceptual field

Developing Probable Event Scenarios

With a rich, multi-perspective material about the studied object, it’s easy to see that it is both excessive (with extra details) and insufficient (missing important links). These properties of information allow you to start combining data into coherent scenarios: omitting something as an extra detail, searching for something as a missing link.

Example of probable event scenarios

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The process of accumulating knowledge is iterative, progressive, and cyclical by nature. Understanding a question accumulates, clarifies, details, and… becomes clearer.

This process can be seen as systematic work with data. For example:

So, the main tasks of the information analysis algorithm are:

• Developing the habit of analyzing the object both as an independent element and as part of a system;
• Seeing information in both general and detailed forms;
• Being ready to clarify and specify research questions;
• Evaluating information in terms of its systemic position in the context of data about the object;
• Being able to combine information (not just compile text fragments with Ctrl+C — Ctrl+V), i.e., conducting cognitive experiments using carefully selected information to create coherent structures (scenarios).

The deeper a person delves into a topic and tracks connections and details, the more vividly the image of the studied object emerges. The effect described in Bayes’ theorem can be observed in one’s own learning process. The object of study comes alive in the researcher’s understanding, nuances are revealed, insights occur. Meanings become accessible and a little clearer. Step by step, a person approaches a new state: understanding meaning. When this moment comes—no one knows: for some, it takes years; others may progress faster. Remember that any modern process of cognition is burdened by the abundance of false information about the world. The world is immersed in information “garbage” of various sizes and qualities. Of course, even in a landfill, you can sometimes find something valuable, but the process will be slow. You need to learn to create your own Order of Understanding of the world. And remember: unless you learn to separate the wheat from the chaff, you cannot move forward into the world of understanding.

To be continued in the next Infovation article.