What Goes Up Must Come Down

 

“Why do you think those adverts on TV are repeated so often? “

Many years ago this question was posed in an informal discussion, quite out of any context, by scientist and TV personality Professor Heinz Wolff.  It emerged from an insatiable curiosity about everything that is a hallmark of both the engaged scientist and innovator.  Memories of the day have long faded, except for that question.

Photo by P. G. Champion

Professor_Heinz_Wolff

The fact that such a mundane question can be posed by so eminent a scientist suggests that the answer might be important.  The same TV advertisements are indeed broadcast repeatedly.  They are expensive and so the benefits of doing so must be appreciable.  We have now reached a point to sketch an outline of what an answer might look like.

Adverts communicate information that enables TV viewers to perceive the value of their associated products or services.  In the vocabulary of this website, the broadcast information creates instances of Consumer Product Interaction with a consequent consumer perception of value.  A viewer may feel a closer association with their sporting hero by wearing the brand he is commissioned to promote.

The raising of value perceptions has been imagined as the elevation of a huge, wobbly marque-like structure referred to as a Value Surface, on which a single point marks an individual’s response at the time of that Consumer Product Interaction.  This value perception may subsequently go up or down depending on whatever information follows.  News of a drug scandal degrades the image of the sportsman and his sponsored products.  The Value Surface in its entirety is forever in an agitated seascape motion that is hopefully nudged upwards by each broadcast advertisement.

One mechanism of action for the advertisements is through the classical conditioning discussed in the previous post.  Just as the dogs of Pavlov could be taught to associate the sound of a bell with the arrival of food, the information and images in the advert may evoke an equivalent response in value perceived for the associated products or services.  Recognition of brand attributes has been associated with conditioned responses by Janiszewski and Warlop (2013) .

The requirement to frequently rebroadcast the same information clearly indicates that the association with value needs continual reinforcement.  This could be necessary to increase the number of recipients or enhance value perceived, that is to increase the overall breadth or height of a Value Surface.  TV advertisements certainly have a huge reach.  It is also possible that residual perceived value following the advertisement broadcast might naturally diminish without the subsequent reinforcement of the message.  One might consider that the advert has a role in propagating ideas as a meme, as discussed in New Economics of Innovation, which is analogous to the viral propagation of genetic information.  At any time there are many other possibly conflicting memes competing for survival in the consciousness of a recipient population.   Advertising agencies and media companies alike have done well out of this silent struggle.

With so much content passing every second through the information environment, markets might well be naturally forgetful.  We will explore this idea in more detail later.  At this point we should note that it is often hard work to raise a Value Surface and then to keep it aloft.  This effort appears in the investments and endeavours made by an enterprise and entrepreneur in making their information valuable.  Next we will transform the Value Surface into a single point that represents perceived value for a whole population of consumers.

 

The Neural Roots of an Economic Trajectory

The Value Surface concept discussed above and elsewhere is essentially a micro-analysis of the behaviour of a population of consumers[1].  This is needed to construct and interpret the viscoelastic-derived model that has been used to simulate the commercial operations of an enterprise.  To provide a more macroscopic view of that enterprise, we can collapse the three dimensions of the Value Surface into a single point that is representative of the entire value-creating innovative activities at a particular moment in time.

Taking the average across the multiple perceptions of value for a consumer population gives a single measure of height to which a product can be elevated by the endeavours of an enterprise[2].  Clearly some products will be more difficult to raise than others. The resistance to this elevation will be different for different products, and will inversely represent how attractive they are to their consumers. We can then follow this point of average perceived value with time to draw a trajectory of a product through an economic space.  The role of the enterprise is to “fly” the product through this space, as described in the companion Foreground papers: An Economic Trajectory  and Flightpaths and Forgetful Markets.

Here, with more freedom to speculate, we will consider the neural origins of a potential field through which an economic entity might fly.

In Valoris Cognita Barcelona we have considered how a person’s brain may interpret information received through the senses to build and adapt a continuously changing model of reality.  Consciousness may be considered to arise from an elimination of the errors that separate neural cognition from external reality.  It is a descent down an “error surface” to a point that is satisfactory for survival, but which inevitably leaves some unimportant residual error with an associated subjective perception.

Whilst the electrical impulses and neuro-chemical transmission of information that arise in neural cells and synaptic connections is clearly powered by the host individual, a neural model of reality may be changed by this information flow to become less transient and more ordered to some degree by the memories it engenders.  This explanation needs help from an analogy with another energy dissipative system.

In Writing the Information we considered various systems through which energy flows and thereby transforms the system that is its conduit.  One such example was the flow of water down a mountainous terrain.  The water flow itself was driven by its inherent potential energy acquired due to its height.  The direction of flow is not random but clearly follows a path to descend to the lowest point as fast as possible, and in doing so it sculpts distinctive river valleys into the terrain that serve to further accumulate and direct the water flow and erosion.  In summary, whilst the flow originates from the potential energy of the water and is transient, semi-permanent features are created and remain long after their creation.  In these features are written the full history of previous torrents through the energy dissipative erosive events they have engendered.

Could the energy flows through neural circuity have left similar features that we call memories through associated energy dissipative events?

Water Erosion Pattern

Could the information content that is etched into a brain and into individual neural models of reality be the source of potential energy fields we have hypothesised as resisting the elevation of a Value Surface and its associated Economic Trajectory?

Could a continuous neural remodelling response to new information together with the competition for vital attention explain why signals that are not reinforced dissipate leading to a natural forgetfulness of markets?

Can innovation that is making information valuable actually be a physical phenomenon or should we be content to have a suitable physical analogue of this fundamentally social process?

The latter question may not be as outrageous as it may first seem.  We have discussed in Semantics of Information that deletion of information has thermodynamic consequences.  There are similar issues in other domains for which information may have a physical manifestation.

 

Applying  Landauer’s principle to the information content of the universe  M. Paul Gough (2008) calculates that an information energy makes a significant contribution to the dark energy that is hypothesised to have determined the dynamics of the expanding universe throughout its entire history.

 

NASA and the European Space Agency.
Hubble_ultra_deep_field_largeNASA’s Hubble telescope reveals around 10,000 galaxies with the deepest view into the history of the Universe.

So why are adverts on TV repeated so often?

Imagine a world without the repeated marketing needed to encourage product purchase.  The dynamics of an economic trajectory indicate that goods may not simply remain frozen in space.  The potential retained within the Value Surface has already been diminished by previous Sale Events.  Now, the inevitably dynamics of descent will be brought into play and there will begin an increasing rate of loss of information in a reversal of the previous ascent phase of the trajectory.  Investment and prices will fall as companies seek to convert the residual potential in the collapsing Value Surface into income and consumers will forget how they once valued the goods.

This dynamic mechanical analogy provides an explanation for why commercial television repeats and repeats again advertisers’ content.  It provides an explanation of why salespersons continually need to repeat and reinforce the value proposition of their products.  So finally we can propose a derivative of the Labour Theory of Value Creation where labour is also working to prevent the depreciation of that value due to a natural forgetfulness of dynamic markets.  Innovation has a role therefore, not only in making information valuable in the first instance, but also in retaining the value of the information and opposing a tendency for that value to decline with time as a product matures.

As it is with commodities and technologies, so it is with whole companies that integrate the value of their various commercial activities.  The value of the company as it appears in its stock market valuation depends ultimately on the effective deployment of capital to propel the company on its own economic journey.  The forgetfulness of the market appears in the fluctuating patterns of valuation of public quoted stock.  Memories are short and a regular injection of good news is needed to counter a fall in value.  Volumes of press releases evidence this common practice. The similarity of the qualitative nature of the economic trajectories of commodities, technologies and companies, whilst these may differ substantially in time and duration, together with the voraciousness of consumers to absorb this “vital” information, point to a similar cause and effect arising from the action of an economic potential field.

 

Multiple images of beautiful independence might well convince a receptive individual of the transcendental properties of a particular perfume.

In doing so, their need to be repeated on multiple occasions may be essential to overcome a natural forgetfulness of the fragrance market.

 

 Dior j'adore sm

 Notes:

[1] It may be considered equivalent to the statistical mechanics description of the thermodynamics of a liquid or a gas, which is at the origin or models of economics developed by Paul Samuelson et al.

[2] Some consequences of taking an average measure of value perception are considered in A Labour Theory of Value Creation.

Ubiquitous Error Elimination

Evolution by Error Elimination:  There is one feature on the landscape of innovation that has already been recognised and which will arise again in the future, time and time again.  It appears in all innovation management and evolutionary systems.  It is essential for the creation of new knowledge and in the perception of its value.  It is a fundamental process in the building of models and the fitting of these models to the real world.  These are some the guises of the ubiquitous Error Elimination.

In its most fundamental form Error Elimination appears in the epistemology of the philosopher of science Sir Karl Popper.  In The Logic of Scientific Discovery (1934), Popper recognised an asymmetry in the nature of knowledge that whilst no amount of empirical evidence can prove an assertion to be true, a single case alone may prove it to be false.  It follows that no theory can definitively be proven to be true.

In later work Popper went onto explore how scientific knowledge, which originates in the subjective mind of the scientist, goes onto become an “objective” feature of the world.  In Objective Knowledge: An Evolutionary Approach (1972) Popper develops a “three-worlds” view in which all physical artefacts are “World 1” objects and subjective thoughts and ideas belong to “World 2”.  Popper’s “World 3” is populated by things originating through the human mind but which have gone on to have an existence beyond the confines of that mind.  These include abstract concepts, the content of all books, designs, theories, etc.

Popper’s Three WorldsPopper’s Three-Worlds Relationship

Combining the approach to challenge the validity of existing theories with empirical tests designed specifically to bring about their failure, together with the creation of objective scientific knowledge for those theories that survive this ordeal of falsification, led Popper to conclude that scientific knowledge creation proceeds through an evolutionary sequence:-

Problem 1 >> Tentative Solution >> Error Elimination >> Problem 2

 

Here, the tentative solution to the initiating problem is continually refined in the light of new empirical evidence, until the new data fundamentally conflicts with existing knowledge, which gives rise to a new problem for the cycle to repeat.

 

Error Elimination Creates Value by Risk Reduction:  In earlier work we have extended the evolutionary epistemology of Karl Popper to reach technology innovations that might emerge from the scientific research upon which the original work of Popper is based (Egan et al., 2013, Williams et al., 2013).   This involves an explicit recognition of a subjective Value Appreciation stage which forms the link between subjective World 2 and objective World 3 in Popper’s evolutionary knowledge theory.

Indeed, for scientific knowledge, Popper describes such a value appreciation that is achieved through inter-subjective testing, expert peer review and publication and through which the knowledge becomes objective.

4-Point Innovation Cycle

Popper’s evolutionary epistemology cycle, including an explicit identification of Value Appreciation

Initially, there is often a high risk that a Tentative Solution will not consistently resolve its initiating problem in practice and proof of concept projects are required to understand and manage this risk.  This conforms to Popper’s Error Elimination stage the output of which may comprise accumulated information on designs, and the technical and commercial evaluations from which to conclude the potential benefits and residual risks of an innovation.  In fact, the reduction in risk though Error Elimination can be interpreted as a creation of value through innovation, as it is the value that is perceived by the consumer of this information.

In terms of the previous “Green Box of Innovation” that provides a generalisation of an innovation process based upon enhancing the value of information, it is the parameters of the “box” that determine the operational form through which input information is transformed into outputs that have utility and value.   Maximising the value of the outputs is once again an application of Error Elimination to discover the parameters that provide the best operational form for the Tentative Solution to resolve the real world problem it is tentatively designed to address.

In a direct analogy with the growth of scientific knowledge, the existing Tentative Solution should be repeatedly challenged.  The empirical information will continue to provide evidence of utility and thereby continually adjust perceptions of value.  Hence, feedback loops operate through which the value of the Tentative Solution can be enhanced through the Error Elimination process.

 

Error Elimination by Least Squares:   An innovator may deploy a powerful cocktail of creativity, intuition and experience to make a Tentative Solution relevant and valuable by Error Elimination.  Computers are not gifted with such human capabilities, but on the other hand they excel in their relentless ability the crunch numbers.

The Least Squares method is one of a number of numerical optimisation techniques whereby outputs of a computer simulation can be ‘fitted’ to real-world data.  To do this, some starting values of the model parameters are selected, without knowledge, and a simulated behaviour is derived.  The simulated outputs are compared with real-life and the difference is a measure of the error of that simulation.  This initial error can indicate how to adjust the model parameters to achieve a better fit to the empirical data.  The Least Squares approach enables a further better guess at the model parameters and onward thus rolls an iterative process of Error Elimination to continually improve upon the match between the simulated and the real, to minimize the error and hone in upon parameter values that may provide a new insight into the real world through the window of a best-fit model and its parameters that now describe real behaviour.

Error Elimination we have seen to be part of the process of innovation.  With the Least Squares method it becomes an algorithmic procedure to navigate an error surface.  It works as follows.

It is as though a blind wanderer is placed into a mountainous terrain (for a two parameter model, where the error is a vertical third dimension) with the task of finding the point of lowest altitude, for at this point of minimum error there can be found some useful insight.  Her tool is a stick of enormously variable length through which she can perceive the elevation of the surrounding landscape.  Down steeply sloping hillsides her stick will extend to accelerate descent and avoid the confusion of small rocky undulations.  Into the valley her guide is shortened to follow a meandering contour, always descending towards her goal.  When the topography becomes tortuous, progress is restricted to very small steps, frustrating advancement as the blind wanderer must squeeze through each crevice eventually perhaps to expose wider valleys.  Finally, when all around is higher from the shortest to the longest reach, the wanderer may wonder if she is at the unique point of minimum error.  The wanderer may mark that spot and start again and then again from distant and disparate origins to confirm uniqueness[1], although this might not be necessary.  She may have acquired a valuable insight.

Watching the Least Squares algorithm operate in the virtual world of a computer, it is easy to imagine the numerical model as a blind wanderer seeking the best fit to measurements of reality.  The patterns of descent show a striking resemblance to those previously described in “Writing the Information”, although the topography of an error surface runs through n+1 dimensions, where n is the number of model parameters.   However, this complexity is not relevant for the Least Squares algorithm as Error Elimination proceeds just as it would in our familiar three dimensions.

In an ideal world the final error could be completely eliminated.  It would be an unmistakeable match of a perfect model with perfect data.  Yet all measurements contain their own errors (noise) and in the output of all worldly processes the primary signal is polluted by artefacts which confound perfection with ambiguity.  Also, all models must necessarily be simplifications of the real world, with a judicious ignorance of secondary and tertiary influences.  A perfect model of the real world requires the real world to be the model. For the innovator, it is sufficient to be close enough for practical purposes.

So the innovator must still contribute an essential human element, to innovate upon the structure of the model to better conform to real world observations.  The investigator thus enters into a liaison with the computer to become an n+2 dimension of a hybrid man-machine error surface, which must be navigated to make the model converge towards reality.  Here the inventor is the creative agent giving the model its operational form and the innovator contributes by forging the relationship of the model with reality.  And there may be as many models as pictures hung in a gallery, for value is not in the picture itself but in the understanding gained of its subject.

It is perhaps surprising or even problematic that an automatic computer routine such as Least Squares may be suggested as a means or even a metaphor for innovation.  However, it is not a paradox if the algorithm works on new inputs, so that the path taken to descend the error surface is new and may lead to new and potentially valuable insights.  Of course if this is repeated using the same inputs it would be repetitious and nothing of value could emerge.  Nor is there any accumulation of value as the original path descends to the point of minimum error, as it is only when this point is reached that any value is realised in the insight provided by the “best-fit” model parameters and outputs.

In all the above cases innovation is making information valuable through a process of Error Elimination.  That analogous mechanisms appear in both human and machine applications suggests that the process of innovation itself may not be an entirely social phenomenon.

 

Notes:

[1] This may be considered to be a rather trivial instance of Popper’s challenge of falsification.

Green Boxes of Innovation

 

 FabiusCOP21

Thank you M Laurent Fabius and all 195 nation’s delegates for your achievements at COP21 in Paris today.

It is now time to pull together threads from earlier discussion to sketch a morphology for a generic system for innovation.  Our earlier Black Boxes of Innovation can be combined with the idea that innovation is information made valuable through bioenergetic transformations. The black boxes then become green.

 Green Box of Innovation In this generic system of innovation, inputs may include empirical information, other raw materials and energy.

These inputs are transformed in the Green Box into outputs comprising information that has been made valuable.  This information may be embedded in products and services by commercial organisations.  It may be conserved in the generic code and epigenetics that confer survival benefits to organisms.  It may be the content of a newspaper or the legislation to govern a society.

The parameter values set the operational state of the Green Box.

The creation of valuable information through innovation and the replication of this information through production are intrinsically coupled within the Green Box[1].

The number of parameters can vary from zero to very many, each providing an extra degree of freedom or variability to the Green Box operations.  Indeed the structure of this website has been designed around the concept of a Green Box with no parameters, as it provides a qualitative commentary that connects inputs to outputs.  A more quantitative or algorithmic association between inputs and outputs may use parameters as operational variables for the Green Box – in weather forecasting for example.

The change of colour is used to indicate that the Green Box is not simply an automatic means of processing selected inputs into useful and valuable outputs.  There are additional features that together establish a process of Innovation that is Making Information Valuable:-

  1. There is a dynamic search for relevant input information
  2. There is an integration of various inputs into a coherent interpretation, which may legitimately be challenged at any time by new findings and the output information appropriately modified
  3. There is an optimisation through which the value of the outputs is maximised for the available inputs
  4. Information creation through innovation and information replication through production are to varying degrees coupled within an innovation system

The above four points specify a generic behaviour for an abstract innovation system.  In “Writing the Information” we consider how this behaviour arises from energy throughput and transformation in physical and biological systems, and we have extended this concept into the economic and societal domains.

 Tree Innovation  tree production

The tree as a Green Box:  Information creation through innovation (left) and information replication through production (right) are coupled in this biological system

An analogous behaviour of innovation systems with a tree-like morphology can be observed in various real physical, biological and economic settings which are considered below.

53484_femur_sm Wolff’s law reveals that bones are continuously remodelling to enable a skeleton to adapt and be optimised to support loads to which it is exposed.  In the ends of longs bones, such as at the hip joint of the femur shown here, there are filamentous tree-like trabecular structures of cancellous bone that channel the complex forces that are transmitted across the joint into the stiffer and stronger cortical bone below.

  1. It is a dynamic system where input information regarding physiological loads determines whether trabeculae are reinforced or resorbed
  2. The cancellous trabeculae combine to form an integrated structural system
  3. In healthy bone the system is optimised to provide sufficient strength for normal physical activity with lowest physiological cost
  4. The trabecular geometry is broadly similar for all individuals from the same species

Remodelling also occurs in many other tissues through “mechano-transduction” mechanisms that are largely unknown, although collagen fibrils and tree-like proteoglycan structures have a role to play.  Numerical models are being used to explore this specific Green Box of innovation.

 cardiovascular system With every heartbeat blood gushes through the widest lumen of the aorta and outwards into the body, through finer intermediate arteries and down to the finest capillaries.  This tiny capillary blood flow delivers oxygen and energy to the cells of adjacent tissues.  Those tissues cut off from this energy supply will atrophy and die.

  1. Cells can produce angiogenic factors that call for the creation of new capillaries to enhance blood supply when this is needed
  2. The cardiovascular anatomy is a highly integrated physiological system
  3. Bloodflow can shut down preferentially in peripheral regions at times of stress to protect the vital organs
  4. Cardiovascular anatomy is broadly similar for all individuals from the same species

After the delivery of oxygen and energy is made, the vascular system will collect the outflow, which re-emerges in a series of confluences back into the mainstream venous bloodflow, back to the heart to become re-energised and to repeat this cycle through which energy is continuously allowed to flow to sustain life.

BBC News logo

Google logo

Le Monde logo

 

Twitter logo

News items emerge from interesting events in minor crevices of society and are transmitted through social networks and regional agencies into the major conduits of national and international press.  In this way stories are selected to fill the columns of newspapers which flow out through their distribution channels to find their way onto the doorsteps of a nation the following morning, and onto the web pages of computers at any time of the day.

  1. The search for the latest scoop is a never-ending quest for the journalist profession
  2. Various information sources are used for corroboration to “stand-up” a story in a respected publication
  3. Fierce competition and a 24/7 news cycle with fast developing technologies for information dissemination imply the need for continual optimisation of reporting operations.
  4. Dissemination architectures are relatively fixed and through which the press mixes the creation of new information with the copying and distribution of the latest news.
 container port Since the 1950’s the innovation of the shipping container has been responsible for a remodelling of transportation networks around new ports that provide docking hubs for massive container ships.

  1. The old ports such as London and New York have been displaced to Felixstowe and New Jersey
  2. Tree-like transport infrastructures grew out from these container ports to convey the goods from manufacturing centres, and in reverse to deliver the transported goods to the shops and homes of consumers
  3. Whilst the new container ports grew in size and capacity, the old ports atrophied and died through disuse in an analogous manner to bone remodelling.
  4. Whilst the daily operations of a container port are the repetitive actions of a production system, the development of the infrastructures around the production hub provides one element of innovation in this system
 Big Ben And so it is with politics that one can identify analogous innovative patterns.  Votes cast into the ballet boxes of democracies elect representatives who themselves channel their authority into Government by an executive, by a cabinet, that exercises their rights to develop policies that flow out through the tributaries of the state, back into the constituencies and on into the homes of the voter.

  1. Policies evolve with time aiming to attract the interest of an elector population.
  2. A manifesto integrates policies into a coherent package of information intended to be of sufficient value to be “sold” in return for a vote.
  3. The information content and the dissemination networks are in a continual state of flux to gain the furthest reach into an electorate.
  4. Anyone who has worked on the telephones and doorsteps at election time understands the need to continually repeat policy benefits.

Taxes flow in a parallel conduit, from each member in that same society through into the huge central fiscal channel of the Government treasury, whereupon they are redistributed outwards according to the particular ideologies and macroeconomic priorities of the exchequer, back into the many small niches of the society from whence they came.

Innovation and production are economic activities.   In his Économique et mécanique Leon Walras sought to explain that it was of little importance that physical phenomena can easily be measured whilst economic ones cannot  …. because with each exchange, consciously or unconsciously, a person will know deep down whether his needs are satisfied or not in proportion to the value of the goods exchanged.  Innovation and production within the generic Green Box can be explored using models and simulations of this perception and exchange of value.

 

Notes:

[1] The special cases of pure innovation and of pure production are described in “A Labour Theory of Value Creation

Black Boxes of Innovation

At various points in the Foreground and Background pages of this website key points arise from “models” and “simulations”.  What do we mean by these terms?

Essentially, models are constructed to explore aspects of the physical world that are not directly measureable or accessible by the senses.  In fact, one might even consider that normally such simulations may be employed to interpret what the senses might actually sense – but more on this later.

A model may conveniently be considered as a “Black Box”, with inputs that are directly measureable or sensed.  This Black Box may have parameters that control its inner workings and its outputs provide new information[1].  Of course the input information may be nonsensical or the model operation erroneous, making any new information arbitrary and useless.  Outputs need to be useful for the model to be valuable.  Take a television, for example, where the input radio signal is received through an aerial, the inner circuits are tuned to receive a particular frequency, the numerical content is decoded for the channel (parameter) that is selected and the output is displayed to inform and entertain the viewer.  It is not necessary to understand exactly how a television works to appreciate its value.

Innovation itself may be treated using a Black Box approach, by converting empirical observations and scientific research into information that has utility and value.  In this application we have connected the models that make up the tools of scientific research to the mysterious Black Box through which value created through the researcher’s endeavour is appreciated.

Black Boxes of Early-Stage Innovation

For further information see: When Science Meets Innovation: a new model of research translation

We have modelled the subject of value appreciation using a Value Surface that maps perception of value across a statistical population of consumers.  The elevation of this Value Surface in a hypothetical economic potential field provides a means to link investment and the innovative endeavours of an enterprise to value created.  Let us consider why a model might be useful to explore such a potential field.

 

A Flat-Earth Person Finds Dimension Three

Imagine that you are a flat-earth person.  Not one of a conventional, three-dimensional kind who is able to believe that at some point you may slide off the edge of our saucer-shaped planet.  Rather imagine that the force of gravity in some way acts to compress your perception of height to an infinitesimal thinness.  You will still live on Earth as you do today, but how different your view of the planet would be.  You would truly be a two-dimensional person.  Let us consider what you may see and how you will fit these observations into an understanding of your world.

If you are initially resident on a horizontal plane, then this surface will stretch out before you.  At various points in the distance the altitude of the terrain may change.  Any increase in height, even if this is just a gentle slope, will be seen as an impenetrable barrier.  Likewise, a real lower piece of ground will appear as a hole to oblivion.  You will see the edges of these barriers and holes as lines of constant height, just as contours appear on an Ordinance Survey map.

Your task today is to move to your next appointment, for which you have a map giving the details of your journey.  This map may be a length of string containing paired instructions of distance and direction.  First go 0.7km at 123 degrees, which should bring you to a hill.  Your perception of this hill as an insurmountable barrier does not change but something strange happens as you begin to climb.  The plain on which you approached instantly disappears from view. Facing you now is a solid vertical wall, behind is a limitless abyss.  You only perceive the linear contour that wraps around either side of the hill on which you climb and disappears from view.  But something is happening as you move forward.  This contour is constantly changing and, more importantly, you are using energy although this does not seem to be having any effect.  You are not afraid as the steps you are taking and their associated changes of contour are all precisely detailed on the map you have, so that you cannot possibly be lost!

Finally and suddenly, the hill you have climbed breaks into a plain and again your full two-dimensions of perception are restored.  In the distance the new plain stretches away to further barriers and holes.  Following your map, a descent into a hole is the reverse of the hill you have just climbed.  You step into the abyss whilst behind, a perceived contour gives shape to an otherwise impenetrable barrier.  But caution must be exercised here.  Some holes, the really steep ones, should be descended with care and are best avoided.  Energy is returned too fast to be easily dissipated by your bodily processes.  And then there are the ‘strange’ holes.  These are clearly identified on the string-map as areas to be avoided at all costs – few return from such a descent and, when they do, they are strangely wet.

Shadows lengthen as you move across the new plain.  The impenetrable barriers radiate darkness and as the day moves into evening, this dark-radiation grows in intensity until the effects from all barriers superimpose.  It is important for you to reach your destination before this complete darkness falls.

Before you fall asleep, satisfied with your two-dimensional endeavours of the day, you find time to unfurl another coil of string.  It is a popular book on genetics and evolution and you learn from the string of characters how wonderfully optimised you are for two-dimensional survival by your genetic template.  The mechanism is a model of natural selection through the duplication of a molecular double ziz-zag.

Your perception of the two-dimensional world is likely to be something akin to a large department store.  It is a collection of planes containing interesting artefacts but separated by these impenetrable-appearing barriers.  Up or down you must go to gain access to new vista.  Rather like the opening of an elevator door.

But then someone, an innovator, imagines that gravity is acting as a potential field perpendicular to your perception.  Such potential fields fill their space with a force that pulls or pushes onto things which enter their vicinity.  Magnetism is one example.  In this case, it is proposed that a constant downward force of attraction is acting as you move upwards through this field.  The height you have gained is then proportional to the energy expended in the climb[2].  This is a breakthrough.  All two-dimensional surveyors now have to do is monitor the energy required to reach every point on the impenetrable barriers to calculate their height.

The method the two-dimensional cartographers used in charting their hidden territory was to move to a higher altitude and send back a signal to the starting point giving information on the height gained.  Conservation of energy provides the basic principle.  The tools of the cartographer are a ramp of fixed length L and a heavy cylinder of mass Mc.[3]  The ramp is used to reach up to a point of higher altitude as shown in the figure below.  The cylinder is then released from the top and carries the signal revealing the height h to the bottom.

cylinder-ramp model

The cylinder and ramp of the two-dimensional cartographer

The ramp-cylinder system shown above is the cartographer’s Black Box within which the conservation of energy principle operates: –

Potential energy at the top     =  Kinetic energy of the rolling cylinder at the bottom

=  Translational kinetic energy + Rotational kinetic energy

Mc.g.h  =  ½ . Mc.Vc2  +  ¼ . Mc .Vc2

= ¾ . Mc .Vc2

So that h = ¾ . (Vc2 / g )

Here Vc is the velocity of the cylinder at the bottom of the ramp.  Unfortunately, this velocity is not easy for our two-dimensional cartographers to measure, as the cylinder appears from nowhere to clatter off the end of the ramp.  Some further analysis is needed and this is provided by the mathematical tools of calculus, which convert the measurement into one of time t from the moment the cylinder is released to the point it reaches the base of the ramp.  In this case: –

h = (3.L2)/(g.t2)

and         Sin (a) = h / L

All the mapmakers now need to do is to time the arrival of the cylinder and apply the above equation to know the height at which it was released.  They may then move onto another point and repeat the process, to gain a full knowledge of the height and the slope a of their surrounding terrain.

The conversion of potential energy into the kinetic energy of the descending cylinder is used to create the information content of the two-dimensional map.  The Black Box that provides the two-dimensional cartographers access to their third dimension contains a model that converts measurements that they can make, time in this case, into something meaningful that they cannot measure directly but which they need to know.

In principle models can be valuable, but only when they provide useful insights into the real world.  And their value, like that of any product, will diminish as they become superseded by alternatives with a greater acuity of vision, as the cycle of innovation rolls on.

 

Notes:

[1] Strictly speaking the output information is not new but is a new interpretation of the input information.

[2]   Potential Energy = Mass .g. Height, where g is a gravitational constant equal to 9.81 m s-2 .

[3]   The cylinder is a three-dimensional object and thus causes something of a problem for the cartographers.  It is selected as a linear object with the special property that it will roll along the ramp.

New Economics of Innovation

Yes, one might be forgiven for concluding that innovation is generally a good thing.  And generally, in the longer term, this might well be the case.  Niccolò  Machiavelli though aired his doubts in 1513 in The Prince, in which he observed that:

 Portrait_of_Niccolò_Machiavelli_by_Santi_di_Tito …..  there is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things. Because the innovator has for enemies all those who have done well under the old conditions, and lukewarm defenders in those who may do well under the new.

Not much has changed in half a millennia.  In the short-term the beneficiaries of innovation are often less numerous with far less to loose than the stakeholders of an agreeable status-quo.  Innovation can be regarded as a nuisance, rather like the Asian hornet.

Around 2004 hibernating queen hornets (Vespa velutina) awoke in Lot-et-Garonne, South West France far away from their Shanghai origins, having been transported in a shipment of Chinese pottery.  The bees of Southern France were soon under threat from this voracious predator, which can surgically detach their head, legs and wings to feed the remaining high-protein thorax to their young.  In the favourable climate of Southern France, by 2010 the species had spread into 39 departments and had crossed the border into Spain.  Apicultures had been ravaged.  A new species has entered French fauna and the distressed indigenous inhabitants now just have to learn to live with it.

The disruptive invasion of species into ecosystems is well known.  Rodents from the ships of early explorers of the pacific island and the rabbits of Australia are two examples.   Biologists have extended the principle into the social and cultural domain.  The meme has been proposed by Richard Dawkins in The Selfish Gene as the cultural equivalent of the gene, through which plays out a competition, a natural selection and replication of the most favoured ideas and dominant cultural phenomena[1].  Vast advertising budgets are committed to the transmission of high value memes as brand names compete for their place in the psyche of a society.  In earlier less health conscious times, it was well known that “a Mars a day helps you work rest and play”.

Just as the biologists have ventured tentatively into the social domain, economists have travelled in the opposite direction, as the disruptive and potentially destructive effects of innovation can similarly destabilise an economic environment.

The term “creative destruction” was adopted[2] by the economist Joseph Schumpeter in his 1942 book Capitalism, Socialism and Democracy to describe this impact of innovation that “incessantly revolutionises the economic structure from within, incessantly destroying the old one, incessantly creating a new one”.  Whilst Schumpeter denounced any direct association with biology, his work became one of the foundations of an emerging discipline of Evolutionary Economics that seeks deploy biological analogy in economic analysis.

In fact, the second half of the 19th century was a time rich in the sharing of ideas and metaphors between biology and economics.  Charles Darwin is said to have found inspiration in the challenging proposals of Thomas Robert Malthus on economic survival in inevitable circumstances of limited resources.  The theory of the evolution of species by natural selection developed by Darwin was extended into sociology by Herbert Spencer and inspired the American economist Thorsten Veblen to consider how the preferences of individuals evolve in relation to their economic environment.  Veblen went onto expound the concept of conspicuous consumption through which the wealthy choose to consume expensive goods as an overt display of their wealth, presumably unaware of the memes that were propagating through their behaviour.

As the methods and models of conventional neoclassical economics grew in sophistication and widespread adoption through the first half of the 20th century, the alternative interpretation of evolutionary economics fell into disrepute.  The discipline had become tainted by association with the atrocities of eugenics.  Its rehabilitation and re-emergence followed Schumpeter’s publications in which innovation was cited as a source of variation upon which act the forces of natural selection to determine which products, firms and entrepreneurs survive and thrive in an ever-evolving marketplace.  The assumptions are quite different from the neoclassical theories of marginal supply and demand.  Endogenous stimuli of innovation are continually disturbing the economic equilibrium on which the latter are based.  The adventurous risk-taking entrepreneur is at odds with the neoclassical rational economic agent.  It is not surprising, therefore, that there is no great consensus both within evolutionary economics itself, after all it is a relatively new field of enquiry, and between evolutionary economics and its more established rivals in economic analysis.

Along with Schumpeter, the Austrian-British economist and philosopher Friedrich von Hayek considered how the abstract rules, organisational routines, practices and know-how of firms and societies can represent their intellectual and cultural DNA, which has evolved to become increasingly effective in facing up new challenges, mainly through trial and error and in a direct analogy to Darwin’s principle of natural selection.  Apprenticeship and training then become vital for passing on this key economic survival information to future generations.

The entrepreneur is the agent that introduces variation and disrupts an economic equilibrium.  Selection also operates at this level favouring those entrepreneurs with the capability of overcoming the hostility and inertia identified by Machiavelli in “ the incredulity of men, who do not readily believe in new things until they have had a long experience of them.”  Both individual, social and political factors combine in this selection process.  In this economic evolution it is often new technology that embeds current savoir faire in the disruptive innovation and the interventions of the entrepreneur that instigates a continuous evolution of know-how as a dynamic process of technological and social change.  Successful practices will reproduce and become the automatic routines of firms and individuals, until the next innovation shockwave disrupts the evolved status quo.  This, according to Schumpeter, is the essential dynamic of the capitalist system.

After decades of somnolence, since the 1980s following the publication by Nelson and Winter of their opus An Evolutionary Theory of Economic Change, the discipline of evolutionary economics with a specific focus on innovation and its role in development has taken off with a large and creative array of tools and analyses.  The perspective is of a continuously evolving marketplace, where uncertainties prevail and reliable information is at best limited.  It is realistic whenever economic rationale of organisations must also be imperfect.  Survival then depends on the response of organisational routines and know-how that lead to the decisions that maintain sustainability and profitability.  This dynamic differs from the classical view of firms acting to maximise their profits on the basis of fully informed rational decisions.  The essential routines form the acquired memory of each organisation, existing in the competences of its individuals and the collective knowledge embedded in its products and systems of production, both of which must evolve and be called upon variously to maintain a sustainable operation.  The spread of best practice will diffuse successful routines, which must mutate to integrate in different organisations and environments.

Innovation is therefore both internal to the firm in a continual modification (mutation) of its constituent routines as well as in the external drive to innovate to survive as described by Schumpeter.

Remington QWERTY 2

The QWERTY keyboard and the VHS video recorder are classic examples of how the adoption of innovation does not necessarily favour the optimum or the most advanced outcome.  Other factors come into play.  Feedback loops operate where training and familiarity lead to easier use of a sub-optimal technology, which may then find its utility extended into new areas.  Partnerships can expand the scale of adoption and thereby reduce costs.  Supporting technologies grow around established innovations – the apps for the ipad – enhancing their dominance.  Like a howling loudspeaker, such feedback has an effect of amplifying the sound of small incremental steps that can become highly influential.  Again a crucial role of the entrepreneur is in facilitating adoption.  Survival is not necessarily guaranteed for the fittest innovation but is likely for the most widely adopted.

 

Notes:

[1] A detailed account of the action of memes can be found in The Meme Machine by Susan Blackmore, Oxford University Press.  1999.

[2] The term was apparently first used by Karl Marx for a different mechanism of capitalist economic development

Short Definition of Innovation

It is with mild trepidation that we seek a definition of Innovation.  It is possible to become so locked into the complexities and controversies of this definition, and onwards into what is and is not innovation, that we might become bogged down forever in the semantics.  We should therefore seek something sufficiently generic hopefully to be atraumatic, even though we must sacrifice some precision.

The Oxford Dictionary of English defines innovation as “the action or process of innovating”, and less circularly as “a new method, idea, product, etc”.  The root verb to innovate is to “make changes in something established, especially by introducing new methods, ideas, or products”.  The 16th century origin of the word is from the Latin verb innovare that fuses “into” and “make new”.

Wikipedia offers a deeper insight into the definition of innovation to be (at the time of writing), “the creation of better or more effective products, processes, technologies, or ideas that are accepted by markets, governments and society”.  Now a review of the evolution of this Wikipedia entry on innovation reveals the underlying polemic over this definition.  Nevertheless, the discussion ventures to address aspects of innovation that are important for organisations where the following definitions are proposed:

  • “[Innovation] is generally understood as the successful introduction of a better thing or method. [It] is the embodiment, combination, or synthesis of knowledge in original, relevant, valued new products, processes, or services.”
  • “Innovation is the multi-stage process whereby organizations transform ideas into improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace.”[1]
  • “All innovation begins with creative ideas. […] We define innovation as the successful implementation of creative ideas within an organization. In this view, creativity by individuals and teams is a starting point for innovation; the first is a necessary but not sufficient condition for the second”.
  • “Innovation, like many business functions, is a management process that requires specific tools, rules, and discipline.”

It becomes clear that innovation can take many forms according to its context.  It can apply to products and processes.  It can enhance performance or simplify practice.  It can increase value or reduce cost.  It is borne by teams of investigators co-ordinated within large projects or by individuals who might have a good idea.  Innovation thus presents itself as a multi-faceted catch-all term that embraces all things which can be considered to be progress, at least in an economic sense.

Rather than depend on a heuristic set of specific examples to describe what innovation is and explain how it should be done, there is a need before going further to fix a-priori a definition to establish our own perspective.  Such a definition, as sparse and as general as can be delivered as this time, is as follows:-

 INNOVATION  is Making Information Valuable

 

One might immediately detect a fix.  The incertitude surrounding innovation is being transferred into the doubly intangible terms of Information and Value.  Nevertheless, we have our starting point and a part of what follows is a justification of this somewhat cryptic definition.

With this definition we can associate innovation with two closely related concepts: invention and risk.  Invention, or more broadly discovery, is the creative act that provides an initial idea, at the heart of which is information that at this point of creation has an uncertain value.  This is the initial risk of the invention.

Thomas Edison famously defined genius to be 1% inspiration and 99% perspiration.  The creative act of invention is the 1% component.  Innovation then demands 99% of one’s genius to be dedicated to developing the value of that information of discovery.

Value here can take its classical forms of use-value or exchange-value, the former representing the direct utility of the invention, where the latter provides for an exchange of the invention for other goods that actually are of use.

We will go onto develop this notion of value as we proceed with our discussion on Innovation.

640px-Thomas_Edison2

Thomas Alva by Louis Bachrach, Bachrach Studios, restored by Michel Vuijlsteke [Public domain], via Wikimedia Commons

One might be forgiven for concluding from the above discussion that independent of its definition, innovation like healthcare is generally welcome.  However, history proves this to be far from the case.  This we will discuss in our next post.

 

Notes:

[1] Baregheh A, Rowley J and Sambrook S.(2009) Towards a multidisciplinary definition of innovation, Management decision, vol. 47, no. 8, pp. 1323–1339

Science of Innovation

At noon on 8th September 2011 anyone in need of advice on the subject of innovation need not to have looked far.  Within 0.2 seconds Google was able to find 326,000 results by searching for “Innovation Consultancy”.  Amazon listed 44,841 books on the subject of innovation.

Today 30th March 2015, Google now tracks 47,800,000 Innovation Consultancies in 0.35 seconds and Amazon has 71,478 innovation books on its virtual shelves.  A quick look through the links and titles reveals a huge diversity in vision and process promising a spectrum of technical and social outcomes.  The innovation landscape is growing fast and has many features.

In all this diversity there is not a strong sense fundamentally of what innovation actually is.  It is Swiss army knife of a concept, where some interpretation can be found to service a particular need or provide a universal panacea.  However, this absence of theoretical roots that establish a foundation for innovation can lead to a superficial and often opportunistic proliferation of approaches and ideas based generally on heuristic processes “proven” to work in the real world.  It would surely be the case with any of the sciences without their own theoretical foundations and conceptual models.

This points to a need to establish some theoretical foundations on which subsequent processes and practice of innovation can be built.  We must first develop an Innovation Science before translating these principles for application in the real world by Innovation Engineering.

Immediately we run into a serious difficulty.  The sciences stand more or less as distinct disciplines and islands of knowledge that are secure and mutually consistent within their strict practice of critical peer review.  Progress from these rather feudal roots into a more federated and integrated union of the human intellect is in its very early days and has a long way to go.  Innovation however is an amalgam of sciences, humanities and the arts.  A theoretical basis for innovation must involve concepts of value, information, cognition, creativity, transaction and so on.

What is required is no less than an integrated framework through which the primary domains of the sciences, the humanities and the arts can link their syntheses and analyses to provide the required theoretical foundations for an innovation discipline.

Such a venture is worthwhile because innovation is important.

Around two million years ago the move by Homo erectus from creating and using rough Oldowan stone tools to more sophisticated and symmetric Acheulean tools, could reasonably have been cited as a major innovation. The process and examples have continued ever since.

 Oldowan Tools  Acheulean Tools

Oldowan Tools:  1.7 to 2.4 million years ago

Acheulean Tools:  0.4 to 1.7 million years ago

As a concept, innovation goes back in economic thinking.  In the first few pages of his seminal publication An Inquiry into the Nature and Causes of the Wealth of Nations (1776), Adam Smith recounts a story of a boy employed to open and close a valve to enable a steam engine to operate.  This boy found that the same action could be achieved by means of a piece of string attaching the valve to another part of the machine.  Thus, “one of the greatest improvements that has been made upon this machine, since it was first invented, was in this manner the discovery of a boy who wanted to save his own labour”.  Smith goes on to consider in detail links between innovation and wealth creation, interestingly without ever mentioning the word itself.

 Obama 25-01-11 P012511PS-0738President Barack Obama delivers his State of the Union address  in Washington, D.C., Jan. 25, 2011. (Official White House Photo by Pete Souza) Innovation has become a word much used by leaders to inspire and motivate the men and women they lead to undertake what is needed to improve their lives, their work and their society.  In his first State of the Nation address to the US congress on Jan 26th 2011[1], President Barack Obama made reference to innovation as the first of three cornerstones for the future economic prosperity of his nation (the other two being education and infrastructure).[1] http://www.whitehouse.gov/state-of-the-union-2011

So innovation indeed has a long history which has left its traces in the progress that human civilisation has made since its origins. This offers the opportunity for an historical and even archaeological analysis, and it is through such a study of intellectual and social evolution, and where possible linking this to its biological equivalent, that innovation can be observed straddling the sciences and the humanities.  By uniting these disciplines we are able to explore the fundamental nature of innovation as a concept and as a process.

A next step will be to enquire on the definition of the word, and to propose one definition of sufficient capacity to enable us to proceed.