The Human Lens

The human lens is modelling language for human social behaviour that allows us to understand living systems and to reason about such systems. It consists of thirteen categories that are invariant across cultures, space, and time. The human lens provides a visual language and reasoning framework for transdisciplinary collaboration. The human lens allows us to make sense of the world from a human perspective, to evolve our value systems, and to structure and adapt human endeavours accordingly.

The human lens is comprised of:

(1) The system lens – a simple modelling language for complex adaptive systems. It supports the formalisation and visual representation of knowledge and resource flows in complex socio-technological systems based the three categories of resources, events, and agents (the REA paradigm, an accounting model developed by E.W. McCarthy in 1982 for representing activities in economic ecosystems). The system lens can be applied at all levels of organisational scale, resulting in fractal representations that reflect the available level of tacit knowledge about the modelled systems.

(2) The semantic lens – a simple modelling language for articulating purpose and value systems, to make sense of the world and the natural environment from a human perspective. The semantic lens support the formalisation and visual representation of values and economic motivations of the agents identified in the systems lens. The semantic lens provides a configuration framework for articulating ethical, cultural, and economic value systems as well as a reasoning framework for evaluating socio-technological system design scenarios and research objectives with the help of the five categories of social, designed, symbolic, organic, and critical.

(3) The logistic lens – a simple modelling language for articulating value creation and recycling activities, to structure and optimise human activities within a given culture. Its support the formalisation and visual representation of value creating activities and heuristics within socio-technological systems. The logistic lens provides five categories for describing value creating activities: grow (referring to the production of food and energy), make (referring to the engineering, and construction of systems), care (referring to the maintenance of production and system quality attributes), move (referring to the transportation of resources and flows of information and knowledge), and play (referring to creative experimentation and other social activities). The logistic lens can be used to model and understand feedback loops across levels of scale (from individuals, to teams, organisations, and economic ecosystems) and between agents (companies, regulatory bodies, local communities, research institutions, educational institutions, citizens, and governance institutions). The categories of the logistic lens assist in the identification of suitable quantitative metrics for evaluating performance against the value system articulated via a configuration of the semantic lens.

The three parts of the human lens are connected via a SECI knowledge creation spiral (socialisation, externalisation, combination, internalisation) and via the proliferation of belief systems and design practices that humans have developed over thousands of years as part of specific cultures, which are continuously evolving.

The human lens can be used to model all aspects of the relationships between agents (humans, human organisations, and even non-human biological or human designed agents) and all aspects of collaboration between two or more agents. Furthermore the fractal characteristic of the human lens allows the representation of groups of collaborating agents and the representation of abstract relationships between such groups. Thus the human lens allows for the representation of all collaborations encountered within the web of life on this planet to the level of fidelity that reflects our collective understanding.

Human lens modelling tools

A suitable modelling tool that supports the human lens can be implemented with the help of any meta modelling language to the extent that it provides support for the axioms of category theory and denotational semantics.

A basic implementation is achievable with a UML modelling tool and the configuration of a UML profile that includes suitable stereotype definitions for the thirteen human lens concepts. An even more basic implementation is afforded by markers and a whiteboard or by pencil and paper, but then of course the models can’t be used to drive automation and agent based simulation tools.

The full potential of the human lens can however only be harnessed with a tool like the Cell Platform that provides an unlimited multi-level instantiation capability and that enforces strict semantics for agent based modelling.

The Cell Platform provides a clean formalisation based on the axioms of category theory that is recursively bootstrapped from the structure of an ordered pair, without any spurious complexity induced by the underlying implementation technology (the JVM). Additionally the Cell Platform:

  1. uses denotational semantics (a unique machine readable semantic identity for each concept) to completely separate the concern of naming from the concern of semantic modelling, allowing each agent to introduce preferred labels and symbols
  2. enables communication and collaboration between agents based on artefacts (information resources), and events which equates to native support for the REA paradigm
  3. provides an API in the language of category theory that exposes the recursive construction and that hence allows extensions of all concepts
  4. allows agents to make selected models discoverable, to make selected models visible to other agents, and to declare semantic equivalences between concepts in different models that are recognised by the reasoning engine within the Cell Platform
  5. provides support for 4-state information quality logic (true, false, unknown, not applicable) to allow agents to easily process incomplete data and any structures they may find in the models from other agents – without resulting in ambiguous semantics
  6. supports logic and reasoning entirely within the abstract language of category theory, since semantic equivalences are defined between semantic identities rather than between human assigned labels