[...] Economics is not typically thought of as a global systems problem. Indeed, investment banks are famous for a brand of tunnel vision that focuses risk management at the individual firm level and ignores the difficult and costlier, albeit less frequent, systemic or financial-web problem. Monitoring the ecosystem-like network of firms with interlocking balance sheets is not in the risk manager’s job description.
A parallel situation exists in fisheries, where stocks are traditionally managed one species at a time. Alarm over collapsing fish stocks, however, is helping to create the current push for ecosystem-based ocean management. This is a step in the right direction, but the current ecosystem simulation models remain incapable of reproducing realistic population crashes. And the same is true of most climate simulation models: Though the geological record tells us that global temperatures can change very quickly, the models consistently underestimate that possibility. This is related to the next property, the nonlinear, non-equilibrium nature of systems.
Most engineered devices, consisting of mechanical springs, transistors, and the like, are built to be stable. That is, if stressed from rest, or equilibrium, they spring back. Many simple ecological models, physiological models, and even climate and economic models are built by assuming the same principle: a globally stable equilibrium. A related simplification is to see the world as consisting of separate parts that can be studied in a linear way, one piece at a time. These pieces can then be summed independently to make the whole. Researchers have developed a very large tool kit of analytical methods and statistics based on this linear idea, and it has proven invaluable for studying simple engineered devices. But even when many of the complex systems that interest us are not linear, we persist with these tools and models. It is a case of looking under the lamppost because the light is better even though we know the lost keys are in the shadows. Linear systems produce nice stationary statistics—constant risk metrics, for example. Because they assume that a process does not vary through time, one can subsample it to get an idea of what the larger universe of possibilities looks like. This characteristic of linear systems appeals to our normal heuristic thinking.
Nonlinear systems, however, are not so well behaved. They can appear stationary for a long while, then without anything changing, they exhibit jumps in variability—so-called “heteroscedasticity.” For example, if one looks at the range of economic variables over the past decade (daily market movements, GDP changes, etc.), one might guess that variability and the universe of possibilities are very modest. This was the modus operandi of normal risk management. As a consequence, the likelihood of some of the large moves we saw in 2008, which happened over so many consecutive days, should have been less than once in the age of the universe.
Our problem is that the scientific desire to simplify has taken over, something that Einstein warned against when he paraphrased Occam: “Everything should be made as simple as possible, but not simpler.” Thinking of natural and economic systems as essentially stable and decomposable into parts is a good initial hypothesis, current observations and measurements do not support that hypothesis—hence our continual surprise. Just as we like the idea of constancy, we are stubborn to change. The 19th century American humorist Josh Billings, perhaps, put it best: “It ain’t what we don’t know that gives us trouble, it’s what we know that just ain’t so.”
Among these principles is the idea that there might be universal early warning signs for critical transitions, diagnostic signals that appear near unstable tipping points of rapid change. The recent argument for early warning signs is based on the following: 1) that both simple and more realistic, complex nonlinear models show these behaviors, and 2) that there is a growing weight of empirical evidence for these common precursors in varied systems.
A key phenomenon known for decades is so-called “critical slowing” as a threshold approaches. That is, a system’s dynamic response to external perturbations becomes more sluggish near tipping points. Mathematically, this property gives rise to increased inertia in the ups and downs of things like temperature or population numbers—we call this inertia “autocorrelation”—which in turn can result in larger swings, or more volatility. Another related early signaling behavior is an increase in “spatial resonance”: Pulses occurring in neighboring parts of the web become synchronized. Nearby brain cells fire in unison minutes to hours prior to an epileptic seizure, for example.
The global financial meltdown illustrates the phenomenon of critical slowing and spatial resonance. Leading up to the crash, there was a marked increase in homogeneity among institutions, both in their revenue-generating strategies as well as in their risk-management strategies, thus increasing correlation among funds and across countries—an early warning. Indeed, with regard to risk management through diversification, it is ironic that diversification became so extreme that diversification was lost: Everyone owning part of everything creates complete homogeneity. Reducing risk by increasing portfolio diversity makes sense for each individual institution, but if everyone does it, it creates huge group or system-wide risk. Mathematically, such homogeneity leads to increased connectivity in the financial system, and the number and strength of these linkages grow as homogeneity increases. Thus, the consequence of increasing connectivity is to destabilize a generic complex system: Each institution becomes more affected by the balance sheets of neighboring institutions than by its own. [...]
"ORGANIZATIONS WILL NEED TO PLAN for increasingly chaotic environments that are out of their direct control, and adaptation must involve adjusting to all 10 of the trends (listed below)", observers Gartner fellow and VP, Tom Austin.In a report published earlier this year titled "Watchlist: Continuing Changes in the Nature of Work, 2010-2020", Gartner says that organizations will need to determine which of the 10 key changes in the nature of work will affect them the most, and consider whether radically different technology models will be required to address them.
Work will become less routine, characterized by increased volatility, hyper-connectedness, 'swarming' and by 2015, 40 percent or more of an organization's work will be "non-routine," up from 25 percent in 2010.Later next month, Tom Austin is scheduled to speak in London on these trends:
Some are lucky enough to fulfil the highest of Maslow’s [top need from his psychology theory of the hierarchy of needs], self-actualization, at work. All sorts of people find true fulfilment at work – software developers, recording artists, even auditors. But it is a lot to ask from a job. Others, perhaps most people, hope for work that is reasonably interesting, and indulge their true passions – singing, hiking, wine-tasting – on the weekends.[Emphasis added.]
The best businesses are good at providing a sense of belonging. But belonging can be transient. Businesses succumb to competition and disappear. Or technological innovation makes them redundant. No doubt the photographic darkroom was a companionable place to work; so was a travel agency. There is less need for them now.
I suspect it is Maslow’s second highest need – respect – that people most crave from work: respect not just from their colleagues but from the world [...] and it gets us closer to what business is for: making profits and serving customers by doing something we can be proud of.
Santa and Banta submitted the tender for digging the second underwater Euro Channel Tunnel connecting England with France and continental Europe. This was perhaps the first time that a bid for such an extreme engineering project was coming from India, and apparently so it raised a few eyebrows and steered interest. An outsourcing relationship with India was not a new thing, but bidding for second Euro tunnel - that had got to be special...
Mr. Santa and Mr. Banta, the proprietors of Santa Banta & Co., were also among the main invitees to present their ideas describing their technology, tools, budget and time-lines to the consortium presiding over the project. As it turned out S B & Co had the lowest quotation, the shortest time-line for the project, the simplest possible plan and most straightforward execution using the most standard of tools: Santa would take one team (of a few hundred thousand labourers) digging from England towards France, and Banta would do the same from the opposite direction. The consortium was now specifically interested in the technology that S B & Co would employ like Global Positioning GPS for estimation and co-ordination of their efforts and meeting midway through the English Channel.
Peter Principle: "Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence." [try here for more]
"Business Development is a bunch of activities of today, based on your strategic vision of your product/service framework, that the Sales people would be selling tomorrow."Well, this definition might neither be universal nor be entirely technically accurate. However, it does give a certain level of clarity (when some of the rather experienced folks are contributing to the confusion). To me, these two functions are neither the 'same' nor 'interchangeable', but are distinct. And by the virtue of that clarity one can perhaps define both the functions more accurately and also appreciate their imperatives.
[Above: The 'Arrow-head' components: a) the Sales function as the cutting-edge, b) the Business Development (BD) function, the main-body, that gives the aerodynamic shape and (thus) 'direction' to the arrow, and c) the Pre-sales function that embeds the Arrow-head to the stem (delivery streams).]
The history goes that some hundred years ago, Mr. King Gillette was a wealthy but frustrated failure of an innovator at 40. He had written a book called "The Human Drift", which argued that all industry should be taken over by a single corporation owned by the public, and that millions of Americans should live in a giant city called Metropolis powered by the Niagara Falls.
This really got me thinking as to how a product could sustain its retail price for almost three years while inflation is nearly 7% yoy, and the input costs for the cartridges, mainly steel, have jumped (e.g. price-rise in automotive sector). Even a 1-2% difference in prising would have killed the motive of this post, but here we are, with three probabilities:
Chris talks about six business models with examples that are used in the web2.0 economy, and goes on the indicate that all of them revolves around the concept of "Free!".