Resilience as concurrent interplays of opponents: preliminary ideas and call for collaborations

In my previous work “A behavioural model for the discussion of resilience, elasticity, and antifragility” I discussed resilience as the emerging result of the dynamic interplay between the behaviors exercised by a system and those of the environment it is set to operate in. Then, in my latest work “On ambients as systemic exoskeletons: Crosscutting optimizers and antifragility enablers” I introduced a “horizontal” and a “vertical classification” of systems. The idea in this second paper is that any system may be considered at the same time a “whole”, a set of “parts”, and a nested compositional hierarchy of functional nodes.

By applying the idea of the first paper to this second idea of a system’s horizontal and vertical classifications, I thought of resilience / antifragility as the emergent result of concurrent behavioral interplays taking place across the involved systems’ [ wholes / sets of parts / hierarchies of nodes ].

Let me clarify this through two examples:

1. A bullet is shot and passes through the body of a living being. Such a traumatic event directly affects a number of organs and systems of that being. Interdependence among organs and systems is likely to lead to cascading effects that may in turn lead to severe injuries or the loss of life.
2. A hurricane hitting a region. Catastrophic events such as this one typically ripple across the involved hierarchies of nodes triggering the concurrent reactions of multiple crisis management organizations.

In both cases, resilience may be modeled as the result of the effects of an external event on a system’s horizontal and vertical organization. The external event manifests itself at all systems and networks levels and activates a response that is both individual and social.

Now, I believe a good way to model such response could be in terms of Game Theory. The idea is to consider:

• GT players as the involved whole / parts / nodes (let me call them “opponents”).
• Behavior classes as the strategic choices available to the opponents.
• GT strategies as the behaviors planned and enacted by the opponents.

• Moreover, I propose to assign energy budgets specific of the involved opponents. Said energy budgets serve as global constraints shared by all of the nodes of the GT players across their horizontal and vertical organizations.

• Finally, I propose to associate GT payoffs to the possible behavioral responses, with costs (in terms of consumed energy budget resources) proportional to the complexity of the chosen behavior.

(↦ A simple purposeful behavior only requires sensory / actuator parts, thus calls for less energy than a complex proactive behavior, which requires analytical / planning parts; in turn, that behavior would cost less energy than a complex antifragile behavior, which requires in addition complex knowledge/wisdom management parts.)

The idea I would like to develop is that to define nested compositional hierarchies of payoff matrices: sort of interconnected and mutually influencing payoff “spreadsheets”. Overall resilience may be expressed, maybe in some analytical form, as the solution of those payoff spreadsheets taking into account the outcomes of all the concurrent interplays and the consequences of those outcomes on physically or logically neighboring parts / nodes.

(↦ Think once more of the case of the bullet passing through a living body and of the cascading effects of the many concurrent “confrontations” between the involved organs and the bullet.)

The payoff spreadsheets would also model the local and global costs in terms of the shared energy budgets. Adopting the most expensive behaviors at all levels could result in too high an energy consumption, while being able to “keep control”, and proportion one’s response hierarchies, may translate in the ability to survive longer.  Two very eloquent examples of this fact were provided by Dr. Jeff Washburn here).

I would be happy to discuss the basic ideas and possible “implementations” of the just sketched model. In particular I would welcome discussing with experts in game theory and evolutionary game theory willing to co-write a paper on the above idea for ANTIFRAGILE’16.

ANTIFRAGILE 2016

I'm very glad to announce the launch of ANTIFRAGILE'16, the 3rd Int.l Workshop on Computational Antifragility and Antifragile Engineering. Madrid, Spain, May 23-26, 2016

More information will be made available through the ANTIFRAGILE homepage,   https://sites.google.com/site/antifragile2016/

For any information, do not hesitate to contact me!

Special issue of the Springer Journal of Reliable Intelligent Environments: "Resilient and Antifragile Ambient Systems"

News: NEW DEADLINE: AUGUST 14!

Dear Readers,

 

this is to announce a special issue of the Springer Journal of Reliable Intelligent Environments devoted to Resilient and Antifragile Ambient Systems. The special issue CfP shall soon be announced on the official web pages of the journal, though you may find herein a preview copy. Should you require further information, please do not hesitate to contact me!

Kind regards,
Vincenzo.

“Resilient and Antifragile Ambient Systems”

Resilience (from Latin resilire, “to spring back, start back, rebound, recoil, retreat”) plays a central role in several disciplines. Definitions of resilience can be found in ecology, business, psychology, industrial safety, microeconomics, computer networks, security, management science, cybernetics, control theory, as well as crisis and disaster management and recovery. A general interpretation of resilience may be considered that of  “the ability of a system subjected to faults and changes 1) to continue distributing its services 2) without losing its peculiar traits.

Antifragility goes one step further and suggests that certain ambient systems  could systematically improve their system-environment fit, when subjected to faults and changes. Thus an antifragile system would not “stay the same”; rather, it would “get better” with each new experience.

The engineering of antifragile computer-based ambient systems is a challenge that, once met, would allow ambient systems to self-evolve and self-improve by learning from accidents and mistakes in a way not dissimilar to that human beings are capable of. Learning how to design and craft antifragile systems is thus an extraordinary challenge whose tackling is likely to reverberate on many a computer engineering field. New methods, programming languages, even custom platforms will have to be designed. The expected returns are extraordinary as well: Antifragile computer engineering promises to enable the realization of truly autonomic ambient systems able to

- meta-adapt to unprecedented conditions and circumstances;
- self-adjust to dynamically changing environments;
- self-organize so as to converge to proactively optimal strategies to sustain scalability, high-performance, and energy efficiency;
- personalize their aspects and behaviors after each and every user.

And to learn how to get better while doing it!

As a design aspect cross-cutting through all system and communication layers, antifragile engineering will require multidisciplinary visions and approaches able to bridge the gaps between “distant” research communities so as to propose novel solutions to design and develop antifragile ambient systems ;
devise conceptual models and paradigms for antifragility ;
provide analytical and simulation models and tools to measure a system’s ability to withstand faults, adjust to new environments, and enhance their resilience in the process.

The main topics of this special issue of the Springer Journal of Reliable Intelligent Environments include, though are not limited to:

- Conceptual frameworks for antifragile systems, ambients, and behaviours;
- Dependability, resilience, and antifragile requirements and open issues;
- Design principles, models, and techniques for realizing self-resilient and antifragile systems and behaviours;
- Frameworks and techniques enabling the definition of resilient and antifragile ambients;
- Methods and approaches coupling ambient intelligence with antifragility;
- Antifragile human-machine interaction;
- End-to-end approaches towards antifragile services;
- Autonomic resilient behaviours;
- Middleware architectures and mechanisms for resilience and antifragility;
- Theoretical foundation of resilient and antifragile behaviours;
- Formal modeling of resilience and antifragility;
- Software engineering for resilience and antifragility;
- Architectures and design patterns;
- Machine learning as a foundation for resilient and antifragile architectures;
- Antifragility and resilience against malicious attacks;
- Antifragility and the Cloud;
- Service Level Agreements for resilience and antifragility;
- Verification and validation of resilience and antifragility;
- Antifragile and resilient services and components.

Important dates:
Submission deadline: AUGUST 14, 2015
First feedback: October 2, 2015
Final decisions: Noember 13, 2015.

For inquiries please do not hesitate to contact the Guest Editor, Vincenzo De Florio (vincenzo dot deflorio at uantwerpen dot be).

"Write an abstract of your research plan in Layman's terms"

A leitmotiv in our societies is inflexibility. We have great technology, though our supposedly smart phones are not that smart, solutions are one-size-fits-all, devices are not yet appliances, and so on and so forth. And of course we have organizations, which work of course, and are supposed to serve us; but in fact they do so in a very inflexible way, through rigid hierarchies that lead to inefficiency, long service times, and yes very high costs! In fact they look like aqueducts that lose much water along their pipes.

This seems to be two different things, but in fact I think it is the result of a same mistake: looking at things as individual rater than social systems. If you have to design an individual system, you only consider its relationship with the environment it will be placed into. As resources are limited and constraints are many, you need to face trade-offs: for instance, costs versus functionality, comfort vs. energy efficiency, etc. The net result is systems that are systematically unflexible and systematically fragile. If there is turbulence, meaning that things change rapidly, technology reveals its nature of a sitting duck for change. Now, if you look at social organizations, they are the same: they are the result of trade-offs operated while considering them as individual entities. Thus for instance we have organizations such as the civil defence, the police, and the fire fighters; but they are systems incapable of any interaction, of any cooperation. They can't even share their knowledge, let alone work together! As they are "necessary", constraints mean less to organizations. In the end, we "pump" lots of money in them, in the hope of making them "better", readier, smarter. Unfortunately we get so few in return. This becomes apparent especially in times of crisis (Sometimes one even asks where the organizations are! See for instance “Where in the hell is the cavalry on this one?”).

What is missing across the scales is the social and the "ecological" dimensions. "One man is no man", as they say, though it is also true that one device is no device, and one organization is no organization. Systems, men, organizations-they are all the same: complex Wholes that constitute greater Wholes. Dimensioning of resources while designing an embedded system or a novel social organization should be a social rather than an individual-oriented process. Systems, services, organizations—they should be designed with interoperability in mind. They should be regarded as Wholes and at the same time they should be realized as Parts of greater Wholes—on, on towards an ever greater degree of emergence; of quality, of adaptability, of resilience, of flexibility. This is what my Fractal Social Organizations aim to achieve. This is where I'm directed in my research action for the coming years.

"Write an abstract of your research plan in Layman's terms" by Vincenzo De Florio is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Permissions beyond the scope of this license may be available at mailto:vincenzo.deflorio@gmail.com.

How Resilient Are Our Societies?

Regardless of its nature, any system is affected by its design assumptions. Our societies are no exception. The emergence of sought properties such as economic and social welfare for all; sustainability with respect to natural ecosystems; and especially manageability and resilience, highly depends on the way social organizations are designed. A typical case in point is given by traditional organizations operating in domains such as healthcare and crisis management. A common assumption characterizing those organizations is the adoption of a strict client-server model. This produces at least the following major consequences:

• Stigmatization. Users are permanently classified into service providers and service receivers. Traditional healthcare organizations for instance typically classify users into two disjoint categories: the active users, namely professional and informal carers, and the patients and the elderly, who are considered as users incapable of any active behavior [1]. Likewise, in disaster management organizations, predefined active roles are assigned to institutional responders while the citizens are confined to a passive role [2, 3].
• Fragility. The artificial distinction in an active and a passive side of society severely affects quality-of-emergence [3, 4]. In particular, it introduces a systemic performance penalty in that only a subset of the social actors is available to serve the whole set. As well known, the fast growth and the progressive aging of the human population are introducing a new context—one in which the “service subset” is quickly decreasing in proportion. The problem is no more the ever increasing social costs; rather, it is the fact that the spectre of unmanageability—namely the vision of a fragile society unable to serve its citizens—is just around the corner.
• Absence of a referral service. Despite having an only partial view on the capability and current state of the available servers, it is the responsibility of the client to identify which server to bind to. It is the user that needs to know, e.g., which emergency service to invoke, which hospital to call first, which civil organization to refer to, and so on. Referral services do exist, but they mainly cover a single domain (i.e., healthcare) and very specific cases (typically, the seamless transfer of patient information from a primary to a secondary practitioner [5]). Because of this specialization such services mostly possess an incomplete view of the available resources.
• Lack of unitary responses to complex requests. To the best of our knowledge, no referral service provides a composite response to complex requests such that the action, knowledge, and assets of multiple servers are automatically or semi-automatically combined and orchestrated. Even electronic referral systems in use today are mostly limited [6] and only provide predefined services in specific domains. As a consequence, in the face of complex servicing requests calling for the joint action of multiple servers, the client is basically left on its own. Societal organizations do not provide unitary responses nor assist the client in composing and managing them. Reasons for this may be found in lack of awareness and also in the “convenient” shift of responsibility for failures from the server to the client.

A logic consequence of the above situation is the urgent need to mutate our organizational paradigms and assumptions. Simply stated, we cannot afford anymore not to use the full potential of our societies. This means that the artificial distinction between an active and a passive subset should be removed—or at least significantly reduced. Moreover, the increasing complexity of modern times require that societal organizations assume responsibility for becoming the enablers of collectively intelligent responses. New organizational design assumptions are called for, able to provide us with new servicing paradigms—in other words, new ways to perceive and manage the status quo. The vision of the organization as a system restricting the freedom to play roles should be changed into that of an enabler and a provider. Instead of preventing participation, the organization should allow roles to be filled by whomever is able and willing to participate. More than this, the organization should function as a catalyst of mutualistic cooperation among the role players at all levels, from the citizens to the governing institutions. By means of the organization, knowledge should flow among the players highlighting needs, assets, requirements, and opportunities. The organization should assist in the process of self-orchestrating a response, making it easier for all parties involved to coordinate themselves, exchange information, take the right and timely decisions.

Two key challenges of our societies are, one the one hand, being able to define one such organizational model. At the same time, fundamental aspects of the identity of the organization must be preserved. A second challenge is thus learning how to guarantee the resilience of our “evolved” organizations. Organizational fidelity [10, 11] (bounds to identity drifting) therefore becomes a new design requirement for our future organizational models.

In previous posts I presented an organizational model called Fractal Social Organizations (FSO). FSO takes the responsibility as a generalized referral service for the orchestration of complex social services. In FSO, the rigid client-server scheme of traditional organizations is replaced by service orientation, while bottleneck-prone hierarchies are replaced by communities of peer-level members. Role appointment is not static and directed by the organization, but rather voluntary and context-driven. It is my conjecture that the just stated new design assumptions allow for the creation of smarter societal organizations able to match the complexity of our new complex world.

References

1. Sun, H., De Florio, V., Gui, N., Blondia, C.: The missing ones: Key ingredients towards effective ambient assisted living systems. Journal of Ambient Intelligence and Smart Environments 2(2) (2010)
2. Colten, C.E., Kates, R.W., Laska, S.B.: Community resilience: Lessons from new orleans and hurricane katrina. Technical Report 3, Community and Regional Re- silience Institute (CARRI) (2008)
3. De Florio, V., Sun, H., Blondia, C.: Community resilience engineering: Reflections and preliminary contributions. In Majzik, I., Vieira, M., eds.: Software Engineering for Resilient Systems. Volume 8785 of Lecture Notes in Computer Science. Springer International Publishing (2014) 1–8
4. De Florio, V.: Communication and control: Tools, systems, and new dimensions. Lexington (2015)
5. Kim, Y., Chen, A., Keith, E., Yee, HalF., J., Kushel, M.: Not perfect, but better: Primary care providers’ experiences with electronic referrals in a safety net health system. Journal of General Internal Medicine 24(5) (2009) 614–619
6. Shaw, L., de Berker, D.: Strengths and weaknesses of electronic referral: comparison of data content and clinical value of electronic and paper referrals in dermatology. British Journal of General Practitioners 57 (2007) 223–224
7. Anonymous: Secured health information network and exchange (SHINE) (2012)
8. Anonymous: SHINE OS+. Available online at http://www.shine.ph (2015)
9. Verhulst, E.: Zen and the art of safety engineering. Available online at http://www.slideshare.net/ericverhulst1/zen-and-the-art-of-safety- engineering (2012)
10. De Florio, V.: Antifragility = elasticity + resilience + machine learning. models and algorithms for open system fidelity. Procedia Computer Science 32 (2014) 834– 841 1st ANTIFRAGILE workshop (ANTIFRAGILE-2015), the 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014).
11. De Florio, V., Primiero, G.: A method for trustworthiness assessment based on fidelity in cyber and physical domains. CoRR abs/1502.01899 (2015)

How Resilient Are Our Societies? by Vincenzo De Florio is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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What does it mean that a software system is fragile, robust, or antifragile? Considerations, ideas, and examples

In what follows is my answer to the second question of the Webinar on Antifragility: "Antifragility Webinars: Practice Beyond the Rhetoric!" that I mentioned in my previous post:

 

How do I envision, and how am I actually translating Professor Taleb's antifragility into practice?

In order to answer this question I will need to spend a few words about systems made of software.

Software is nowadays becoming more and more complex; this is because it's becoming easier and easier to create complex applications from off the shelf components. Complexity can be easily manipulated, combined and recombined into ever more powerful software systems. On the other hand said systems become more and more fragile, as there is often little or no guarantee about the quality of the "bricks" one uses for their construction.
 

Are those bricks robust? Are they fragile? Antifragile? There's no easy way to tell and no standard that helps at the moment.
 

So what we have today is gigantic castles of sand that are precariously built, in that their solidity and stability depend on a chain of assumptions: A assumes B is going to be reliable and available, B assumes C and D will work as expected, and so on and so forth.
 

(Regrettably this chain of dependencies extends beyond software. Today's critical infrastructure are based on the same principle and share the same weakness.)
 

OK so what do you do to prevent failures? The typical answer is that of using redundant resources. Instead of using a single component, you use several replicas. If one fails, you use another one. Or you use them all at once and then you select the output based on some criterion — for instance a voting scheme. If there's a majority in consensus, you assume the majority is right.

The key word here is redundancy. To better understand what this word means, let me describe you a videogame.

 

You play General Grant; you want to send an important message to a part of your troops so that they are informed of the next steps in your war strategy.  The message has to go through a battlefield that is under the sphere of action of your Enemy. What do you decide to do?

 

A possibility is, you send a cavalryman with your message. Of course the carrier of your message may be hit; in other words, this is a fragile scheme. 

 

Grant knows better, so he sends several cavalrymen in the hope that at least one will reach their destination. For instance, he may choose to have three cavalrymen. This is a better scheme, 'cause it shields from up to two failures; on the other hand, this is a scheme that does not take into account how the situation evolves on the battlefield. You use three cavalrymen because you think that this number is big enough; but your reference is an estimation of the current condition. In fact, conditions may vary. Say the enemy doubles in number, or is joined by an artillery team that increases considerably its firepower. What then? The three cavalrymen may be all wiped out and the message be lost. If you compare it to sending just one cavalryman, this second scheme is much more robust; and though, this is not at all sufficient to counterbalance changing conditions — conditions that mutate, possibly unexpectedly, and possibly very rapidly. The technical word that is typically used is turbulent environments. A simple robust scheme is one that "does not care too much" [as Prof. Taleb says] about the evolution of its environment, and because of this often ends up caring too little.

What then? Well, if one could track the environment (e.g. the firepower) and the way our current scheme matches the environment (basically, how many cavalrymen are left at any point in time) then one could have a more robust scheme — one that is resilient, namely adaptive to changing conditions. New cavalrymen could be added in dire conditions, and their number could even be reduced through more relaxing conditions.

But this is still not antifragile. In fact, the system stays the same: each time you face the problem you launch the same solution — a solution that is not changed by the experience. What we are trying to do is to change this. To change the software "DNA" after each "run" while taking into account the past runs.
 

What we do in practice is, we use web services (representing our cavalrymen); the system tracks the performance of our group of cavalrymen considering both "the parts" and "the whole": each individual "cavalryman" is tracked (one checks whether he's loyal and trustworthy, and to what extent he is) and the ability of the overall group is also tracked (how close we are to failure and disasters over time). 

 

(For more information about the above schemes and especially on

Distance-To-Failure please refer to this and this paper.)

 

When performance is not satisfactory, the scheme is revised. Not just the amount of cavalrymen, but even the choice of which "cavalryman" to use is constantly revised.

 

(For more information please

refer to this and this paper.)

 

Next steps will be to include machine learning schemes to tell which solution works better and best-matches the foreseen next condition. And we want this match to feed back on the solution itself, and be persisted in future runs. In other words we want to change the "genetic code" of the solution. For instance, instead of individual cavalrymen (webservices), we could learn that the scheme could work well with teams of cavalrymen (webservice groups). Said teams could work as a specialized "organism", with different roles within each group. Instead of working independently of one another, those teams could... team up into a fractal organization of webservices functioning as a Fractal Social Organization.

 

(For more information about

Fractal Social Organizations, please

have a look at my ERACLIOS

posts [a, b, c]  and the papers

here and here.)

What does it mean that a software system is fragile, robust, or antifragile? Considerations, ideas, and examples by Vincenzo De Florio is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
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A Behavioral Interpretation of Antifragility

I have been recently invited to participate to a Webinar on Antifragility: "Antifragility Webinars: Practice Beyond the Rhetoric!"

Quoting from the referenced site, on March 18, 2015 Dr. Russ Miles and Dr. Si Alhir

"will host a panel of practitioners to explore:

• How these practitioners have interpreted Taleb’s concept of Antifragility,
• How these practitioners have translated their interpretation into practice, and
• The results and impacts of their efforts — Practice Beyond the Rhetoric!"

Here I would like to share with you my answers to the first question. If time allows I will address the other two questions the coming days.

So, how have I interpreted Professor Taleb's antifragility?

Mine is a behavioral interpretation, meaning the focus is not on the way a system is structured and constructed. Rather, it is on the way the system responds to change. [cf. the work of Wiener and others that brought to the concept of cybernetics]

This approach focuses on systems and their output, regardless of the nature of those systems. Therefore it applies to biological systems ("beings"); artificial systems (cyber-physical "things"...); and it also applies to collective systems made of beings and things.

Wiener and others used behavior to characterize all types of systems — to tell how smart a system was when facing change. There's a famous paper, called "Behavior, Purpose and Teleology", where they distinguish systems according to their behaviors; they have —

• systems that do not react at all (I call them sitting ducks to change)

• systems that react with no concern about the situation (elastic systems)

• systems that check what's going on and try to adjust to it (adaptive systems)

• and systems that keep track of what's going on and try to "tell the future" (anticipate conditions that could be black swans or maybe gold swans) (predictive / extrapolatory systems).

Obviously if you consider resilience, the above classification is somewhat in line with Prof. Taleb's vision of fragile, robust, and antifragile systems. I say "somewhat" because Wiener & co. did not take into account the effect over time of facing change: the genetic feedback produced by the experience. In other words, a systems' evolvability.

If we want to extend the behavioral classification with Prof. Taleb's antifragile systems, we have to consider an extra dimension. I call this dimension the one of evolving feedback behaviors (EFB) — behaviors, that is, that leave a trace in the system, and actually modify the system. It is important to understand that such systems do not preserve the "self" — their identity. It is more difficult to make sure that such systems "stay the same" — namely, comply to their specifications; behave as expected; and so forth. Is this a big problem? Yes it is. Quoting Professor Hawking, an artificial system that can evolve

"would take off on its own, and re-design itself at an ever increasing rate. [..] Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded." (cf. my post "What System Is The Most Resilient?")

Antifragile behaviors may be considered as a particular type of EFB: one in which the self-modification improves the system-environment fit — one that makes it more probable for the system to survive in the current (or the hypothesized future) environment. (Note that being able to improve one's system-environment fit has nothing to do with guaranteeing that what the system does is "right". In other words, special care must be taken to make sure that the drifting of system identity associated with antifragile behaviors does not translate into "dangerous" or counterproductive behaviors. Some form of safety enforcing  invariants should probably be embedded into antifragile behaviored systems (cf. Asimov's Laws of Robotics)

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With or Without You? An Organizational Dilemma

We often get to know of cases in which the interaction between individual and organization is ineffective. A typical example that often finds the front pages of newspapers is that of people who, for instance due to an accident, are in difficulty when interacting with care service organizations. A few examples may help understanding the matter:

1. Newborn child dies in the ambulance before reaching a hospital.

   After a normal delivery, the newborn accused difficulty in breathing. The doctors tried in vain to locate a hospital where she could be transferred. The emergency service initiated a monitoring in the three hospitals in Catania in which pediatric intensive therapy is available: the Garibaldi, the Holy Child and Cannizzaro. None of the three centers had a free bed. The only hospital in eastern Sicily that responded to the call was Ragusa, more than a hundred miles away, a good hour travel. But the little girl died during transport in a private ambulance.

2. Girl dies of common cold while looking for a hospital.

   [...] A hospital in Milazzo (IT) does not have an Intensive Care Unit, and none was available in any of the other hospitals in the Milazzo province. Eventually, a place is found 200 miles away, in Caltagirone, on the other side of Etna. Two hours driving. A young girl dies.

3. 52 years old man died yesterday aboard an ambulance, while looking for a hospital in which to be operated for a double aneurysm.

   The sick was transported to the hospital Umberto I. There, unfortunately, cardiac surgery was 'locked' for technical issues since March 24. Rossella Moscatelli, deputy director of health at the Umberto I hospitals explains: "For two weeks we have been communicating to all hospitals, including Colleferro, and the emergency service, that the temperature controllers were out of order." These devices are essential to run the machinery for extracorporeal blood circulation during open heart surgery. Yesterday the new temperature controllers, just tested, "were in operation for two planned by-pass surgeries - she adds - and Colleferro was informed only five minutes before of the arrival of an ambulance: as they tried to locate in the city another hospital available, we attempted to stabilize the patient. After about two hours we manage to find one hospital available, the San Camillo. For the patient, however, it was too late. Simona Pasca of “Cittadinanza Attiva -- Tribunal for Patients' Rights” remarks: "If hospitals were better connected to each other, that patient might be still alive. The problems of the [classic] organization of emergency repeat themselves cyclically, and patients continue to die."

Other cases may be found here and here. As can be clearly understood from the above cases and the wise words by Simona Pasca, the challenge here is to tackle this problem and reformulate the care system into a “smarter”, more resilient organization such that the above dreadful cases, and similar ones, do not occur anymore.

I believe a starting point towards a solution may be found through the following reasoning:

If I analyze all the above cases I can see three recurring elements:

1. An event takes place and creates a new “need”
2. The need requires a response from a care institution
3. The interaction event-institution is almost completely managed by the individual or by individual parts of the overall care system. The system does not work as a Whole, but rather as a collection of fragmented units.

The disadvantages of the above scheme are clearly demonstrated by the reported cases. In particular, the limited knowledge that the individual and individual parts of the system have about the available resources and their state often traslates in time-consuming and error-prone queries (“pollings”). An answer to this problem cold be given by the engineering of a novel form of organization: the Fractal Social Organization (FSO) (cf. previous post here.)

Such new organization is based on the following “axioms”:

• Member. An individual or a cyberphysical thing can subscribe to a FSO and become member. Members of a FSO belong to a level of the FSO (a “community”) and refer to a representative of that level (“community representative”). The community representative is the “personification” of that level/community and has a dual nature: it is a member of the community it represents and it is also a member of the next higher level/community.
• Role. A member can take a role, namely accomplish actions belonging to a certain class of actions (surgeon-, driver-, nurse-, patient-specific actions...)
• Notification. A member can send notifications to its community representative. These notifications can be status notifications, requests for service, availability of service, etc.
• Check Up. For each new notification the community representative performs a semantic check: it verifies whether the new notification "enables" actions. By “Action” I mean a function of roles that become active when all the necessary “ingredients” (i.e., roles) are available (cf. Dataflow processing; Tomasulo algorithm, etc.)
• Exception. If an action is semantically labeled as “critical”, the lack of a role triggers an exception: the community representative propagates the notification to the next “higher up”.

(It is important to consider that an FSO can also be a member of a community; this translates into the “fractal” structure of FSOs.)

A new “general scheme of operation” enabled by a FSO is then as follows:

1. An event takes place and creates a new “need”
2. The need requires a response from a care institution
3. The institution acts as community representative and manages the response locally. If an optimal response can be found within the community (or, in other words, all roles can be allocated in the current “region”), the response in enacted and the state is adjusted. If no optimal response can be found, an exception propagates the need and the current response state to the community higher-up. Both need and response “travel” through the levels of the FSO hierarchy until the response is finally enabled.
4. While being executed, new knowledge is accrued both locally and globally. The response is thus refined, re-verified, and if (safer, cheaper, higher performing) alternatives are found, they are considered.

Organizational evolution is clearly a very ambitious and risk-taking endeavor. Among the many challenges that needs to be tackled are the following ones:

• Enable existing organizations to become members of an FSO. This will make it possible for existing organizations, e.g. one dealing with emergency care and one managing civil defense, to join forces and share resources and knowledge.
• Enable fast localization of resources (Example: e-Procurement, as in SHINE: Secured Health Information Network Exchange)
• Enable a “natural” (institutional) use of informal carers, and the social “network” centered around the user (its family, friends, etc...)
• Enable non-discrimination of members (an FSO casts no predefined classes of users, as “classic” organizations have: for instance, primary users, secondary users, patients)
• Enable “best use” of social resources (hospitals, equipment and skilled personnel, ...)
• Avoidance of resources being either underutilized or overutilized (cf. Service queues...)
• Enabling optimal responses to crisis management, in particular with the spontaneous integration of informal responders: cf. COMMUNITY RESILIENCE.

Traditional organizations are becoming more and more inadequate and chaos-prone by the minute. In the light of the new scenarios in which resources are getting scarcer and populations bigger, the “old ways” in the organization of human activities reveal all their limitation. We can't live with them; but we can't live without them either. What then?

The time is Now for experimenting with new and more intelligent ways of organization and management of resources. Turning around the current paradigm and having the organization tailoring an optimal response to the user's requests may provide mankind with one such way.

Evolving human organizations -- and learning how to do so without compromising the identity of the intended services -- is verily one of the greatest challenges humanity shall soon be confronted with.

With or Without You? An Organizational Dilemma by Vincenzo De Florio is licensed under a Creative Commons Attribution 4.0 International License.
Permissions beyond the scope of this license may be available at mailto:vincenzo.deflorio@gmail.com.

Antifragility explained by Hayao Miyazaki

Antifragility explained by Hayao Miyazaki:

"Living means changing [..];
 Still, you don't change.
 You can't drift away from
 the project embodied in your structure."

I think this is the biggest difference between resilience and antifragility:
Resilience is "being at work TO STAY THE SAME".

Identity must be preserved, ergo, the system is not free to evolve beyond its "genetic code" — beyond its specifications, for instance.

Not so with antifragility. Antifragility is "being at work TO GET BETTER IN THE PROCESS".

There, you change, and accept the risks of change — the risks of living.



(originally appeared as a LinkedIn post.)