Not all information is equal. It’s necessary that we agree on this. If by the end of this piece we do not, it’s probably not for you. You may believe that there is a God; you may think that capitalism is a superior economic system; you hopefully know that the earth is not flat. The first statement is a belief. The second is an opinion. The third is a fact. Perhaps a glossary for establishing a common language is where we should start.

• Datum/data. A recorded sensation. Sometimes we represent data with numbers. Sometimes it is recorded via the changing of neural pathways. Nevertheless, data are always records of stimuli affecting a sensor. Data have no meaning. Their semantic value is null. A set of photons hitting a sensor (e.g., our eye’s retina, or a camera’s CCD/CMOS) can be recorded (by the optic nerve, or an electronic processing unit). At this point in time it is meaningless. A set of electrical states in our wetware, or a string of ones and zeros kept in a camera’s RAM.

• Information. Anything that has meaning. For example data plus semantics. Once we (beings of reason, as one Mr. Kant would say) integrate context, interpretation, perception to provide meaning to data, it acquires a semantic layer and becomes information. The same set of data formed by the photons above, can then be processed, compared and integrated with existing information, and attributed with meaning. It is now an image of something, say this text. Even before we, or a machine identifies the meaning of the text itself, the recognition and classification of the data as text is enough to create information. It should also be clear that there is no limit to the number of semantic layers that can be attributed to a set of data. As we read the text we continue the process, integrating both new data and existing information, we imbue them with additional layers of semantics and we create new information. 

• Taxonomy of information. It should be noted that the definitions below describe a spectrum. Moreover, they are not necessarily mutually exclusive. Even worse, they can change in time or with context. 

  • Belief. Anything we hold to be true and that does not require proof, content specific knowledge or expertise, and that is immune to falsifying evidence.

    • Prejudice. See belief. Perhaps the only difference is that prejudice is alway pejorative while belief is not (always).

  • Opinion. Anything we hold true based on reasoning (full, partial or even the illusion of reasoning). As such, opinions should be susceptible to reasoning, falsifying evidence, and in general to being changed. 

  • Fact. Any opinion that has been rigorously scrutinized by content experts and held up and is accepted by the vast majority of content experts to be true.

1. Opinions form a Spectrum. Some of you may feel uneasy with this classification. According to this taxonomy, It implies that scientific theories are not facts but opinions. Here’s what we mean. Our hypothesis that human behavior emanates from two primitive emotional drivers is an opinion. An expert analysis of modern economics is also an opinion, but probably more robust. Even a scientific theory such as general relativity is an opinion. Classifying scientific theories as opinions is not meant to, nor does it, diminish the status or usefulness of scientific theories.  Newton’s law of gravitation was a very robust opinion, and useful theory for the advancement of science specifically and mankind in general. Moreover, it being an opinion does not imply that it is on par with, for example, the opinion that the earth is flat, for more than one reason. First, when a layperson, i.e., someone with no content expertise in geophysics, says that in her opinion the earth is flat, it is very likely that she is not stating an opinion, but rather a belief. The fact that she says it’s an opinion does not necessarily make it so. Moreover, even if she is a content expert in the field, and is in fact stating an opinion, the vast majority of experts in this field disagree with her. 

   It is quite difficult to palate. Our classification allows for the theory of evolution and creationism to belong to the same class and may seem to be supporting the idea that they are equal alternatives in the education system. They are, of course, not. If you find this offensive, go sit in the corner with the other flat-earthers. 

   The solution is to provide a metric that would form a spectrum. Positioning nonsense opinions on the one end and, for example, established scientific theories, on the other. We’re proposing the following parameter for this metric: The rate of support an opinion receives from content experts. Assigning a score based on that would imply that given two opinions that are incommensurable, the one with the higher score is more likely to be true.

   The actual implementation of such a scale may be more complex. For example we might decide to weight the content experts based on their credentials and experience. We are not dictating the exact form of the smart contract. We are delineating the guidelines for the concept.  

2. Content experts. Expertise is a vague term. That is not a coincidence. It needs to be flexible enough to fit a variety of contents and contexts. In the realm of formal knowledge, e.g., science, it’s pretty easy. Content experts are scientists. There are globally accepted credentials (e.g., degrees) and indicators (e.g., publications and citations). However, who are the content experts in, for example, social or political situations. Perhaps an example will help. Imagine that I am a member of a DAO. During my interaction with one of the other members I start suspecting that he might be a bot. I have evidence to support this (however, not enough to make it a fact). I share with the other members my opinion and ask that we expel him (or rather, it). Now, imagine that I am, in fact, dead wrong. The other person is not a bot. Moreover, there are many other members in the DAO that know this, either because they have met him, or because they have other evidence that contradicts my opinion. In that case we would suggest that the members are the content experts. Also, we can rate the content experts, much the same way we can rate scientists, based on, for example, the number and length of interactions they had with that person, the evidence they have, etc. The point is that in that context, a cohort of peers is the set of content experts. If indeed most of them disagree with my opinion, their opinion would receive a higher rating.

The taxonomy we laid out here, as well as most other things we wrote above and in the related pieces, are propositions. As such they belong to the Opinion category. That means that we are open to changing it and welcome debate over it.

The next piece in the series will review the bad idea of mimicking corporate governance by stake-based governance in DAOs

Not all information is equal. It’s necessary that we agree on this. If by the end of this piece we do not, it’s probably not for you. You may believe that there is a God; you may think that capitalism is a superior economic system; you hopefully know that the earth is not flat. The first statement is a belief. The second is an opinion. The third is a fact. Perhaps a glossary for establishing a common language is where we should start.

• Datum/data. A recorded sensation. Sometimes we represent data with numbers. Sometimes it is recorded via the changing of neural pathways. Nevertheless, data are always records of stimuli affecting a sensor. Data have no meaning. Their semantic value is null. A set of photons hitting a sensor (e.g., our eye’s retina, or a camera’s CCD/CMOS) can be recorded (by the optic nerve, or an electronic processing unit). At this point in time it is meaningless. A set of electrical states in our wetware, or a string of ones and zeros kept in a camera’s RAM.

• Information. Anything that has meaning. For example data plus semantics. Once we (beings of reason, as one Mr. Kant would say) integrate context, interpretation, perception to provide meaning to data, it acquires a semantic layer and becomes information. The same set of data formed by the photons above, can then be processed, compared and integrated with existing information, and attributed with meaning. It is now an image of something, say this text. Even before we, or a machine identifies the meaning of the text itself, the recognition and classification of the data as text is enough to create information. It should also be clear that there is no limit to the number of semantic layers that can be attributed to a set of data. As we read the text we continue the process, integrating both new data and existing information, we imbue them with additional layers of semantics and we create new information. 

• Taxonomy of information. It should be noted that the definitions below describe a spectrum. Moreover, they are not necessarily mutually exclusive. Even worse, they can change in time or with context. 

  • Belief. Anything we hold to be true and that does not require proof, content specific knowledge or expertise, and that is immune to falsifying evidence.

    • Prejudice. See belief. Perhaps the only difference is that prejudice is alway pejorative while belief is not (always).

  • Opinion. Anything we hold true based on reasoning (full, partial or even the illusion of reasoning). As such, opinions should be susceptible to reasoning, falsifying evidence, and in general to being changed. 

  • Fact. Any opinion that has been rigorously scrutinized by content experts and held up and is accepted by the vast majority of content experts to be true.

1. Opinions form a Spectrum. Some of you may feel uneasy with this classification. According to this taxonomy, It implies that scientific theories are not facts but opinions. Here’s what we mean. Our hypothesis that human behavior emanates from two primitive emotional drivers is an opinion. An expert analysis of modern economics is also an opinion, but probably more robust. Even a scientific theory such as general relativity is an opinion. Classifying scientific theories as opinions is not meant to, nor does it, diminish the status or usefulness of scientific theories.  Newton’s law of gravitation was a very robust opinion, and useful theory for the advancement of science specifically and mankind in general. Moreover, it being an opinion does not imply that it is on par with, for example, the opinion that the earth is flat, for more than one reason. First, when a layperson, i.e., someone with no content expertise in geophysics, says that in her opinion the earth is flat, it is very likely that she is not stating an opinion, but rather a belief. The fact that she says it’s an opinion does not necessarily make it so. Moreover, even if she is a content expert in the field, and is in fact stating an opinion, the vast majority of experts in this field disagree with her. 

   It is quite difficult to palate. Our classification allows for the theory of evolution and creationism to belong to the same class and may seem to be supporting the idea that they are equal alternatives in the education system. They are, of course, not. If you find this offensive, go sit in the corner with the other flat-earthers. 

   The solution is to provide a metric that would form a spectrum. Positioning nonsense opinions on the one end and, for example, established scientific theories, on the other. We’re proposing the following parameter for this metric: The rate of support an opinion receives from content experts. Assigning a score based on that would imply that given two opinions that are incommensurable, the one with the higher score is more likely to be true.

   The actual implementation of such a scale may be more complex. For example we might decide to weight the content experts based on their credentials and experience. We are not dictating the exact form of the smart contract. We are delineating the guidelines for the concept.  

2. Content experts. Expertise is a vague term. That is not a coincidence. It needs to be flexible enough to fit a variety of contents and contexts. In the realm of formal knowledge, e.g., science, it’s pretty easy. Content experts are scientists. There are globally accepted credentials (e.g., degrees) and indicators (e.g., publications and citations). However, who are the content experts in, for example, social or political situations. Perhaps an example will help. Imagine that I am a member of a DAO. During my interaction with one of the other members I start suspecting that he might be a bot. I have evidence to support this (however, not enough to make it a fact). I share with the other members my opinion and ask that we expel him (or rather, it). Now, imagine that I am, in fact, dead wrong. The other person is not a bot. Moreover, there are many other members in the DAO that know this, either because they have met him, or because they have other evidence that contradicts my opinion. In that case we would suggest that the members are the content experts. Also, we can rate the content experts, much the same way we can rate scientists, based on, for example, the number and length of interactions they had with that person, the evidence they have, etc. The point is that in that context, a cohort of peers is the set of content experts. If indeed most of them disagree with my opinion, their opinion would receive a higher rating.

The taxonomy we laid out here, as well as most other things we wrote above and in the related pieces, are propositions. As such they belong to the Opinion category. That means that we are open to changing it and welcome debate over it.

The next piece in the series will review the bad idea of mimicking corporate governance by stake-based governance in DAOs