As we've seen the Living Algorithm System and Living Systems share many features in common. This article explores yet another similarity between the two systems. Both have a fungible component. Let us explore the meaning of the word fungible as it relates to these two systems.
Fungible is a legal term with the following definition: "(of goods contracted for without an individual item being specified) able to replace or be replaced by another identical item: materially interchangeable: Money is fungible – money that is raised for one purpose can easily be used for another." A can of beans is also fungible in this legal sense because the exact number of beans has not been specified. This characteristic of any can of beans must be ignored in a court of law. This implies that there is an acceptable range of imprecision when considering a can of beans. The specific number of beans can have a wide range of values, as long as the can has the same general net weight as claimed. In this sense the word fungible allows for an acceptable range of imprecision in the application of law.
Drs. Jack Cohen and Ian Stewart in their book, The Collapse of Chaos, stretch the meaning of fungible to describe a unique aspect of living systems having to do with the flexibility of interpretation. They point out that every culture, primitive or advanced, employs certain general words to categorize birds of the same species. This is true even though the birds have an abundance of individual characteristics that separates one from another. The word chicken can be applied to an entire group of birds because individual characteristics have not been specified. A chicken can be large or small, young or old, black, speckled or patterned, and still be referred to as a chicken. To identify a meaningful pattern, for instance the group ‘chickens’, it is necessary to overlook the individual characteristics of each bird.
Cohen and Stewart argue that living systems enlist this sense of fungibility to recognize meaningful patterns in their environment. The term ‘fungible’ suggests that a certain level of ambiguity is acceptable on the individual level if we are to make meaningful statements about the whole. This is the sense in which we will use the word fungible.
In this same sense, we suggest that fungible is a term that aptly applies to all living systems. Why?
Living systems require a certain tolerance for imprecision (ambiguity) in both interpreting and responding to the constant flow of information. In order to recognize patterns, organisms must frequently overlook precise details in order to make rough (read practical) comparisons. ‘One can’t see the forest for the trees’ is a common metaphorical expression that recognizes how a singular focus upon only the precise details can obscure patterns. It seems that living systems recognize pattern by sublimating the potentially overwhelming abundance of sensory details in order to make rough generalizations. This processing of information gives precise details a fungible character, which approximates meaning.
Consciousness requires the raw data of sensory input. However, this data is only useful if it has meaning. Some mechanism must process the raw data for it to make sense to the organism. The ongoing processing of information is a way of attributing meaning to sensory experience, as we move through time. And because living systems move through time, these approximate meanings must adjust accordingly. This essential meaning making process is fundamentally pattern recognition. These patterns provide the basis for the tentative working hypotheses that organisms require for all significant interactions with their environment.
The interaction between sensory input and consciousness requires an interpretative mechanism. This interpretative mechanism must have a fungible nature. In monitoring and adjusting to circumstances that are constantly changing, consciousness has a practical accuracy to it. In fact, it is unrealistic to aspire to a higher level of accuracy. Changing conditions require living systems to be capable of utilizing a wide range of behaviors in order to survive. For instance, Life requires a flexible response to sensory input in order to make an appropriate response to the constantly changing circumstances that are an inherent feature of existence.
The mode of the explanation that describes this behavior must incorporate a tolerance of interpretation that sublimates detail for meaning. This tolerance enables a unique response based upon context sensitive conditions. Due to the ever-changing nature of environmental circumstances, a range of possible answers to a range of ongoing input is required. Any of information processing system that is employed by living systems must have a fungible interpretative mechanism.
Guess what? The Living Algorithm System provides this crucial service. Perhaps this is not so surprising any more - considering how many congruencies there are between the Living Algorithm and Life. Just as with relationships, this is due to the very way in which the Living Algorithm digests information.
As soon as the precise raw data enters the Living Algorithm System, it is dumped into the evolving stew of her ongoing averages, as if it were a spice. These rough averages are combined to form derivative or composite averages (the Predictive Cloud). Imprecision multiplied by imprecision. This imprecision is an asset, not a problem. Zadeh's Principle of Incompatibility, previously explored, states that precision and meaning are inversely related when dealing with the complexity of living systems. Precision up; meaning down, and vice-versa.
The Living Algorithm's composite averages, her Predictive Cloud, consist of the sublimated detail of the raw data. They provides meaningful information that describes the nature of the data stream's current moment relative to what went before. This up-to-date information is crucial for survival. The fragile organism might not survive if there is a significant delay in response to environmental stimuli. Sublimating detail for generality facilitates essential pattern recognition that is at the heart of meaning. To provide a fungible interpretative mechanism the Living Algorithm System sacrifices precision for relevance.
As soon as raw data enters the Living Algorithm System it is immediately dumped into a computational stew and is ‘forgotten’. After making its instantaneous impact upon the moment, the data is absorbed into the System’s ongoing measures – the Predictive Cloud. The data leaves traces of its impact, but its precise features are just a fading memory.
Probability, in contrast, must retain the precise features of each member of his fixed data sets. Remembering each of these values is essential, if he is to adequately perform his primary function. Probability requires the perfect memory of a computer, or at least a ledger sheet, to ‘remember’ the precision of his data points.
Probability’s task, as we’ve discussed, is providing measures that characterize the general features of his data sets. Computing the values of these measures is tedious, some might even say complicated, to say the least. The Standard Deviation’s square roots are never that fun. Psychologists everywhere breathed a sigh of relief when computers entered the scene to compute their statistical measures. Further, decades of schooling are required to understand and employ Probability’s many equations. Living Systems don’t have this luxury. The urgency of the moment demands an immediate response to preserve Life’s fragility.
Biological systems have a difficult time remembering anything that is not relevant. A pile of precise numbers from the past has no meaning, except for what they contribute to the present moment. Because these precise numbers have no relevance, living systems would have a difficult time ‘remembering’ them. In contrast, the Living Algorithm’s Predictive Cloud provides crucial up-to-date information about data streams that could be relevant to survival. Accordingly living systems could more easily ‘remember’ the composite averages (the Cloud) that the Living Algorithm provides. Infused with the emotion of survival, the Living Algorithm Measures are well worth remembering, especially compared with the precise values of non-emotionally charged data points. The Living Algorithm Measures are also easy to compute, just one algorithm and basic math (no square roots).
The memory and computational requirements of the Living Algorithm System are all within the range of any biological system, including cells. The memory and computational requirements of Probability are well outside the range of any biological system – including a genius. It seems safe to say that Probability’s obsession with the precision of his data prevents him from providing Life with the up-to-date meaningful information that is essential for survival. In contrast, the Living Algorithm’s neglect of extraneous detail allows her to provide the fungible interpretative mechanism that Life requires. In so doing the Living Algorithm incorporates ambiguity into her System.
In the series of articles that constitute this study, we have developed the following ideas. Living Systems participate in a consistent interactive feedback loop with their environment. Digesting or extracting information from the constant flow of environmental data streams is an inherent feature of this biological process. The language of mathematics is ideally suited for this function. Accordingly, biological systems require some type of data stream mathematics to digest this information flow. We deem this system the Mathematics of Living Systems.
To qualify as the Mathematics of Living Systems, this data stream mathematics must incorporate some key features. We have symbolized these essential requirements in the words, Immediacy, Relationship, Ambiguity/Fungibility, and Choice. The Living Algorithm’s mathematical system has fulfilled three of these requirements. Could it be that the Living Algorithm System is the operating system that living systems employ to digest and thereby extract meaning from data streams? Based upon the abundance of supporting evidence, the hypothesis that the Living Algorithm System is the Mathematics of Living Systems certainly seems plausible.
Let us suppose that we employ the Living Algorithm to digest numerical data. What about the multitude of information flows that can’t be assigned a distinct number? For instance, what about the relative terms that are so useful for organizing our world, such as lighter, bigger, smaller, or smarter? How well does the Living Algorithm fare with non-numerical entities?
The next article in the series addresses this question. To see how every human utilizes the Living Algorithm formula to predict the future multiple times daily, read The Living Algorithm Algorithm.
To experience Life's reaction to this article, Could Life employ Living Algorithm to digest Data Streams?