Category Archives: Incentive design patterns

Attention-Based Stigmergic Distributed Collaborative Organizations

In biological networks such as ant colonies, bee hives, or termites you see self organization which builds and maintains critical architecture. Human beings can also take advantage of this self organizing mechanism to build and maintain institutions. The process is called stigmergy and to take advantage of this concept fully we have to revisit the fundamental theory of “the firm” as form or organization.

An attention based view of the firm

As human beings we are guided by our attention.  It is also a fact that our attention is a scarce resource which many competing entities seek to capture. In an attention based view (ABV)  of a firm it is attention which is the most precious resource and the allocation of attention is critical to the successful management of the firm. In a traditional top down hierarchical firm managerial attention is considered to be the most precious (Tseng & Chen, 2009), and is an very scarce resource. The allocation of attention within a firm can facilitate knowledge search. Knowledge search is part of the process of producing innovation and is effective or ineffective based on how management allocates their attention.

In the top down hierarchical model of the firm you must rely on managers properly allocating their attention because their attention is scarce. The problem of attention allocation (Tseng & Chen, 2009) and attention scarcity both plague traditional top down firms. In heterarchical flat organizations this may not be true anymore and when stigmergy comes into play it opens a door to a whole new method of knowledge search.

Attention-based stigmergic Distributed Collaborative Organizations

A Distributed Collaborative Organization (DCO) is a new model of human organization which did not exist until recently. Now that technology allows for Distributed Ledgers such as what we see with the Bitcoin blockchain it opens the door to new forms of human organizations such as the Distributed Collaborative Organization. Distributed Collaborative Organizations have unique capabilities and work by utilizing a token which represents “membership” in the DCO. Because of how DCOs are set up the tokens likely do not represent securities as they would if the traditional firm were used.

Attention-based stigmergic DCOs can take advantage of swarm intelligence to direct the attention of the members of the DCO. Stigmergy can be implemented through attractor patterns/attractor tokens, and incentive design patterns, both which would direct the attention and shape the activities of the swarm through simple algorithmic rules written as smart contracts. In this instance as Larry Lessig is famously quoted as saying: “Code is Law” but in a non-hierarchical swarm the user’s attention is the most precious resource.

Because attention is the most precious resource in a swarm there should be a mechanism allowing advertisers, or others, to pay for the attention of individual members within the swarm. In this case the DCO would have to be designed in such a way that attention is treated as extremely scarce, something to be preserved by use of bots/autonomous agents (personal preference swarms?) and automation. These personal swarms or personal drone networks if you’d like to call them that would seek out knowledge and information on behalf of individuals without the possibility of distraction.

These swarms could seek out the best deals for individuals. It could collect an extremely detailed amount of information about each and every product and use algorithms to compare products.  This would allow swarms to evaluate anything from video games, to supermarket food, to stocks, to populate a list and buy, or to apply swarm intelligence to the construction of investment portfolios. All of this leads to a completely new paradigm of human organization through self organizing stigmergic institutions.


Grosan, C., Abraham, A., & Chis, M. (2006). Swarm intelligence in data mining (pp. 1-20). Springer Berlin Heidelberg.
Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82(1), 1-42.
Ocasio, W. (1997). TOWARDS AN ATTENTION-BASED VIEW OF THE FIRM WILLIAM OCASlO. Psychology, 1, 403-404.
Tseng, C. C., & CHEN, P. C. (2009, August). SEARCH ACTIVITIES FOR INNOVATION–AN ATTENTION-BASED VIEW. In Academy of Management Proceedings (Vol. 2009, No. 1, pp. 1-6). Academy of Management.

Sharedropping as a stigmergic operation

What is a sharedropping?

Sharedropping is a practice perfected by the Bitshares community. Stan Larimer discusses the purpose of sharedropping in the article titled Bitshares Sharedrop Theory. This quote below highlights what a sharedrop does:

It’s not about imitating Bitcoin.  It’s about attracting an affinity group.  And once you’ve motivated that group to hold onto your coin, you have eyeballs to sell.  In this case, the value of your coin is tied to the value of your group as a target for other developers’ promotional shares. This is exactly what PTS and AGS holders are: A demographic MUCH more likely to be good supporters.  These block chains are like mailing codes that let you target your shares to the people you want to reach much, much, much more precisely than using Silicon Valley mailing codes!

One of the first successful sharedrops occurred within the Bitshares community. Originally Bitshares was centralized around a company Invictus Innovations which invented the concept of Protoshares. Protoshares at the time represented nothing more than an idea. All who believed in that idea were encouraged to acquire Protoshares through either mining it with their CPUs, working for it, or buying it.

What is a stigmergic operation?

Protoshares represented the hopes and dreams of the Bitshares community symbolized as a token and the developers encouraged all participants to rally around that shared idea by formulating a social consensus. This represents a stigmergic operation and provides one of the best examples of stigmergy to date in the crypto community.

How can you conduct a stigmergic operation?

To do a stigmergic operation just follow these basic steps.

  1.  Come up with a compelling idea and share it with people who are likely to appreciate it. Be good at explaining the idea and make sure people believe it can work.
  2. Find an attractor pattern to represent the idea. This could be as simple as tokenization where anyone can mine if they believe in the idea or acquire the token somehow by buying or working for it. It can also be the joining of a mailing list, the membership on a forum,  citizenship, reputation or anything you want.
  3. Create stakeholders in the idea. This is where you conduct the sharedrop onto all who hold the token, or who are on the mailing list, or who maintained active membership on the forum or virtual citizenship group.
  4. Create a social consensus and or constitution.

Once all is in place you will have created a swarm.  The price of the attractor tokens will influence behavior of the swarm. In bees the duration of a dance is the signal but for humans price is usually the signal. The social consensus is also extremely important to follow consistently because it is the glue which holds everything together. It is trust in the distributed rule-set which holds the holonic structure together.


Incentive design patterns and stigmergic optimization

What are incentive design patterns?

An incentive design pattern is a configuration of attractors which indirectly or directly induce the desired behaviors. Unlike attractor patterns which attract human attention these incentive patterns can communicate signals which may indirectly motivate and coordinate the behavior of human agents but also for non-human agents in a multi-agent system. Stigmergic optimization is possible in these multi-agent systems through these incentive design patterns.

A quote from Wash and MacKie-Mason:

Humans are “smart components” in a system, but cannot be directly programmed to perform; rather, their auton-omy must be respected as a design constraint and incen-tives provided to induce desired behavior. Sometimes these incentives are properly aligned, and the humans don’t represent a vulnerability. But often, a misalignment of incentives causes a weakness in the system that can be exploited by clever attackers. Incentive-centered design tools help us understand these problems, and provide de-sign principles to alleviate them.

As an example while the attractor token might be a cryptocurrency the incentive design pattern effects the autonomous agent and human alike. Both can be incentivised by the configuration of incentives.

What is stigmergy?

Stigmergy is a process of coordination which is used by bees, ants, termites and even human beings. Ants use pheromones to lay a trace which is a sort of breadcrumb trail for other ants to follow to reach food for instance.

Humans can also utilize stigmergy in similar ways. Human beings can use virtual pheromones to lay a digital trace for the rest of the swarm. These virtual pheromones just like with the ants act as a breadcrumb trail. These virtual pheromones are the like attractors.

What is stigmergic optimization?

Stigmergic optimization is how ants find the best route to food by using pheromones to leave traces for all their peers.  At first the trace patterns appear random because the ants try all different routes to reach their goal. Optimization takes place as the most efficient path is found and the pheromone traces allow the ant swarm to learn.

In the context of a multi-agent system the agents focused on acquiring attractor tokens at first would not know the best path to take. All paths would be tried in the beginning as agents follow the trail of attractors tokens to the destination. Over time the most efficient path to the destination would be found by the agents and an order would emerge as a result of stigmergic optimization allowing the swarm to solve complex problems.


Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011, May). Gamification. using game-design elements in non-gaming contexts. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 2425-2428). ACM.
Dipple, A. C. (2015). Collaboration in Web N. 0: Stigmergy and virtual pheromones.
Heylighen, F. (2015). Stigmergy as a Universal Coordination Mechanism: components, varieties and applications. Human Stigmergy: Theoretical Developments and New Applications. Springer. Retrieved from http://pespmc1. vub. ac. be/papers/stigmergy-varieties. pdf.
Obreiter, P., & Nimis, J. (2005). A taxonomy of incentive patterns (pp. 89-100). Springer Berlin Heidelberg.
Wash, R., & MacKie-Mason, J. K. (2006, July). Incentive-Centered Design for Information Security. In HotSec.