Design the new business

About combining the roles of both designers and businesspeople…

 

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A feedback model

In an earlier post I commented on the work of Poiesz. Adopting this methodology leads to the following mental exercise.

According to Poiesz, an individual has intrinsic and extrinsic Motivation, Abbitity and Opportunity that defines behavior. These elements have to be taken into account of one another. Together they mark a behavioral factor: M*O*A = behavioral score.

poiesz_individual

The Intrinsic and Extrinsic Motivation (Mi, Me) together define the overall Motivation. However, there is the possibility that when the internal motivation is firm, it can over rule the external motivation (‘social pressure’, page 65, Gedragsmanagement, Poiesz 1999). Vice versa is assumable as well (frustration). The impact of M is as follows.

poiesz_individuals

Let us assume the individual working in a team with a manager within an organisation.

poiesz_organizations

Let us now propose that the internal factors (Mi*Ai*Oi) are more or less stable and that the external factors are influenced by the persons up the hierarchical ladder. For instance the manager offers a personal appreciation (Ind +Me), a training budget (Ind +Ae) and agrees with the individuals personal development plan (Ind +Oe). In parallel, the director delivers a motivational management speech (Man +Me), additional secretarial support  (Man +Ae) and plans for market diversification (Man +Oe).
Of course, external conditions influence all persons as well (check DESTEP: i.e. competitors, market developments, pension, bonus system, tax rules, climate change, transport).

Zooming in on the individual and the manager, we see an interaction. The individual’s total behavior has an impact on the extrinsic behavior of the manager and likewise the manager’s total behavior has an impact on the extrinsic behavior of the individual:

poiesz_ind_mgr_interaction

The same goes for the behavioral impact between manager and director. Accordingly, the same goes for individuals that represent organisations within an alliance. See an example of 5 partnering organizations, with individual representatives (only 2 interactions drawn):

poiesz_alliance_interaction

This exercise suggests that a persons behavior impacts the extrinsic motivation (Me, Ae, Oe) of both collegues and partners. There is a chain (or actually an interwoven network) of feedback loops of the wholes of the behavior onto parts of the behavior.

For now we see:

  • factorization of individual behavior : M * A * O = F
  • interference of individual motivation: M = (Me+Mi)/2 || M = Me || M = Mi
  • influence from total behavior of person A onto the extrinsic behavior of person B: [M,A,O]e of B *=  [M,A,O]total of A
  • All (n = 1 to i) extrinsic Opportunities are subject to external Contextual factors, each with a unique Impact (j): Oe(i) *= Cf(j)

Note about factorization: this concerns driving new behavior or qualitative steering of existing behavior
Note about influence: the same goes for B ont A, and for all other individuals

A similar exercise using the Aizen model (or Leary’s Rose) would offer interesting material for comparization. Besides real world measurements and data assimilation.

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Plankton theory

Symbioses is known as:

Interaction between two different organisms living in close physical association, typically to the advantage of both (Oxford Dictionary)

Symbioses describes states of inter species relationships. These relationships are described as both close and longterm. They are clasified obligate (nessecary for the survival of the species) or facultative. Beyond the Oxford ‘typically to the advantage of both‘, symbioses has at least 3 types: mutualism, commensalism and parasitism. Mutualism is a state where both individuals of different species derive a benefit. Commensalism describes an interaction where only one individual benefits and the other is not significantly harmed or helped. In case of parasitism one individual benefits and one is harmed.

species_a_and_b

A -kind of separate- type is amensatism; one individual is inhibited or completely obliterated whereas the other remains unaffected. A sapling’s survivalproblem because of it’s position directly in the shadow of a bigger tree is an example of this type. This example is hard to classify, because the sapling can be of the same species as the bigger tree or not.
There is coexistence of both a positive and a negative pay off, respectively longterm evolution of a species (adaptation / survival of the fitest) and shortterm death of an individual.

Huisman, Snoek and Weissing (‘De wiskundige kat, de biologische muis en de jacht op inzicht’, volume 55 Epsilon, by Heesterbeek, Diekman & Metz, 2004, p 181-196) describe how competition can lead to chaos, where chaos to enriches biodiversity (read more @ RUG).
The referred chapter starts with a mathematical description of phytoplankton growth related to the concentration of available nutricients.

In case of one plankton species that grows on a single nutricient, the number of individuals evolves into a limited population, due to the carrying capacity of the pool. Two plankton species that compete for a single pool of nutricients deliver two stable populations, where it is plausible that the low-energetic species has a competitive advantage: the species that is most efficient with the nutricient wins, in this case. This principle is known as competitive exclusion.
In case of two species in competion for two nutricients, Liebigs Law of the minimum (1840) states that the growth speed of a species is defined by the most limiting nutricient. Check: The number of nutricients limits the number of stable states of coexistence. In this case, one state could end up with a single species that deals efficient with both nutricients. Another state is when 2 species coexist, pushing away all other species.
A system with N limiting nutricients can result in stable states (equilibria with 1 to N species) or in unstable states. The authors use the expression: ‘The jack of all trades is a master of none’, referring to the trade offs. Seldomly a species is best on most or all fronts of competition.
Here’s an example of a distrubuted need for nutricients:

species_food

The authors then describe a system of 5 plankton species and 3 nutricients. Small differences in starting conditions of 5 nutricients lead to signifiantly changed configurations. In this case we have an unbalanced situation, where competition leads to chaos, resulting in increased biodiversity. Mathematics can help grasp the outcomes and appearing configurations, but it cannot predict them.

We can ask ourselves if current delicate-balance-approach of complex systems minimizes biodiversity as well. Or will increased biodiversity bring mainly negative side effects? It is plausible assume some impact on continuity or robustness of (individuals within) organizations.
Let us explore a bit further…

Similar to plankton, an organisation can be seen as an entity as well.

coop_food

Even a system of organisations can be compared to a single entity, like plankton.

universities_food

According to ‘plankton theory’, a merge into 3TU or Regional University cluster is only possible and sustainable when there at least more than three limiting nutricients, unless the mergers take a step beyond a delicate balance and embrace an unbalanced set of vulnerabilities.

Each University has a set of nutricients of it’s own: 1st, 2nd, 3th and 4th order money (dutch: ‘geldstroom’), plus a number resources from other institutes (source: dutch government). Although the Netherlands provide one pool of nutricients -with unique characters and conditions- , there is always a bigger fish. The Shanghai ranking and a need for internal efficiency are drivers for the merges of Universities, making possible (1) more students (gaining extra, indirect / external nutricients), (2) shared facilities (efficient nutricient-to-action-ratio A) and (3) a robust / adaptive portfolio of scientific curricula (efficient nutricient-to-action-ratio B).

Let us not forget the time span of ‘plankton theory’ and merges of universities. In a bigger closed system (an ‘earth pespective’), plankton population will rise and decline, whereas in a smaller system (a ‘pool perspective’) it is a matter of life or death for plankton species and Universities, for an individual diatom and Rector Magnificus.

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Embedded Interaction

As Processing coder & Arduino embedder & (recently) HTC user, this 2010 documentary makes me happy… 130.000 sold Arduino’s in 2010!

The 2001 book ‘Where the action is’ by Paul Dourish is coming to life. What if we would use an Arduino to introduce physically embedded ‘loyalty-sliders’ into multi actor, distributed power meetings?
Imagine a sketch like this, connected to physical sensors & actors in a decision making environment:

Screenshot_at_2012-02-10_225031

Suddenly alliance representatives are able to mark and distribute 100% sensed loyalties, igniting a discussion that matters: a discussion about individual, internal and external interests and loyalties

… and instead of using visual sliders, we could automate as well, according to http://frontiernerds.com/brain-hack

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Typology of the opportunities

This blog post is a visual reflection of the current proposal, and works towards an overall typology of interorganizational dynamics.

Let us asume 5 ambitious persons, whose interests have a focus of technology, business and biology.

0_hobbies

Suppose these persons are in fact Rectores Magnifici of (some of) the leading Universities in the Netherlands.

As described in Examples, the Delft University of Technology (DUT) building / consolidating on both a regional & cross domain level and a national & technological orriented level.

1_developments

Note 1: both tactics are supporting the strategy of a higher ‘Shanghai Ranking’.
Note 2: the regional collaboration Delft-Leiden-Rotterdam does not yet have a logo, as far as I know. Therefore I use a common shared regional logo: the logo of the ‘Zuid-Holland’ province.

Let us asume there is a set of cooperations, visualized from the DUT perspective.

2_cooperation

 Beacuse of cooperation, there is competition as well. Running parallel paths (national + technological & regional + cross domain) can be an energy drain for an organization, as well as a mechanism to ensure robustness of the strategy.
It is unclear whether or not a strong regional alliance has a positive or negative effect on the 3TU-activities. And vise versa.

3_competition

It is always good to try and split double headed arrows. As a result we find two types of interests: convergence and divergence. We can identify two supra local (above local) interests, both converging in on the DUT.

4_convergence

We can also identify four ‘cross local’ divergent activities: the DUT is connecting towards other local initiatives.

5_divergence

This example is from the DUT perspective. The local activities as well as the supra local activities in fact are all two way streets. The traffic does not have to meet in the middle and the reach of approaching traffic has an effect on the outgoing traffic. There is a delicate balance of loyaties, intentions and interests.

Within a complex context, people and organizations discover new processes. This is contradicting to complicated context, where a blue print or screen play can be created up front to guide individuals and organizations. During complicated situations a protective role of judical departments is more helpfull, whereas during complex situations an enabling role of objective advisers is better at hand.

This research aims at enlightening a multi actor, inter organizational context in which organizations have to deal with the lack of a single effective command. Such a context demands developing other capabilities of employees and offering a different support to those employees. Strengthening the effectiveness of decision making calls for an unambiguous get-the-pulse system, let alone because of the fact that today numuruous organizations are all at the same time inventing alliance dynamics.
There lies a common ground, a shared but hidden typology of dynamics: these dynamics deal with differentiation over time, place and reach, together with converging and diverging actions, interests, responsibilities, loyalties and intentions of both individuals and organizations.

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