If a factory is torn down but the rationality which produced it is left standing, then that rationality will simply produce another factory. If a revolution destroys a government, but the systematic patterns of thought that produced that government are left intact, then those patterns will repeat themselves….
There’s so much talk about the system. And so little understanding.
–Robert Pirsig
Zen and the Art of Motorcycle Maintenance
Reid Hoffman, Ben Casnocha, and Chris Yeh’s The Alliance: Managing Talent in the Networked Age (find in a library) is a short but engaging read focused on three core ideas for talent management in the “networked” age:
1. Use an “Alliance” framework between employer and employee
2. Invest in and leverage employee networks
3. Encourage and/or run employee alumni networks and groups
The Alliance Framework
The book opens with the usual assertion that the old model of “lifetime” employment is dead. Where it begins to veer from the typical, though, is by frankly criticizing the alternatives seen as replacing lifetime employment: falsely ascribing “family” status to an organization and its members and employees, or fully resigning to a free agent, market-ruled alternative.
Most CEOs have good intentions when they describe their company as being “like family.” They’re searching for a model that represents the kind of relationships they want to have with their employees–a lifetime relationship with a sense of belonging. But using the term “family” makes it easy for misunderstandings to arise.
In a real family, parents can’t fire their children.
The authors instead point to professional sports teams as an exemplar of the Alliance framework. The professional sports team has a specific mission (win games and championships) and members come together to accomplish the mission, even as the composition of the team changes over time.
While a professional sports team doesn’t assume lifetime employment, the principles of trust, mutual investment, and mutual benefit still apply. Teams win when their individual members trust each other enough to prioritize team success over individual glory; paradoxically, winning as a team is the best way for the team members to achieve individual success.
Borrowing a military term, the authors suggest that organizations harness entrepreneurial talent by using a tour of duty framework. They are careful to note that companies are very different from the military: while a departing employee might get a farewell party, a soldier who leaves his unit before his tour is complete is AWOL and gets court-martialed. They argue that the metaphor is still useful, however, since both military and business tours of duty focus on honorably completing a specific, finite mission.
Tours of duty are defined by the specific mission to be accomplished, and not time-in-role as career experience is often reduced to. Tours of duty are also not “one size fits all,” and three different types of tours are suggested:
Rotational Tour of Duty
Typically at the entry or junior level, Rotational tours are not personalized to specific employees. Rotational tours are often used by consulting firms, investment banks, and tech companies who provide standardized on-boarding for new junior employees, often allowing them to rotate through a finite number of roles during the often two to four years of the tour, usually for a predetermined number of months (3, 6, or 9) in each role. The primary purpose of the Rotational tour is to evaluate potential long-term fit on both sides: employer and employee.
Transformational Tour of Duty
Transformational tours are personalized to individual employees and are less about specific time commitments and more about a clear and specific mission to be accomplished. The promise of the Transformational tour is that it gives the employee the opportunity to transform both his or her career and the company by accomplishing something substantive. The crux of the Transformational tour is this win-win synergy for employer and employee. The Transformational tour is personalized and structured at the outset with both the employer’s goal and the employee’s future career aspirations–whether in the current company or elsewhere–front and center.
Foundational Tour of Duty
Foundational tours often occur at the highest (founder/executive) level. Foundational tours occur when there is “exceptional alignment” between employer and employee as a defining hallmark of the relationship, and the employee is identified with the organization and vice-versa (e.g., Warren Buffett and Berkshire Hathaway). Typical tenure in Foundational tours is 10 years or more, though Foundational tours are not restricted to executives, since Foundational tours at all levels ensure ownership, continuity, and serve as keepers of institutional memory.
No one ever washes a rental car. A Foundational employee would never allow the company to cut corners to meet short-term financial goals.
The authors spend the next several chapters of the book carefully laying out the prerequisites and steps for using tours of duty. First, they discuss the importance of defining an organization’s core mission and values so specifically and rigorously that some players feel strong alignment while others feel so out of alignment they might leave the organization. (The authors argue that organizations want to lose this latter group.) Next, they provide specifics on having the kind of honest, raw conversations with employees that are crucial for effectively using a tour of duty framework. Finally, they provide suggested timelines and tools for checking in and using feedback during the course of a tour of duty, as well as negotiating subsequent tours.
Employee Network Intelligence
In the second major strategy in The Alliance, the authors claim that employee networking is a good thing. Rather than seeing networking as a detriment to the organization or a behavioral indicator that an employee is thinking about leaving, The Alliance suggests that employers should pay employees to build, maintain, and leverage their networks. The authors argue that in the current era of knowledge work, human capital is defined not simply by the knowledge, skills, and abilities in each individual employee, but by all that those employees can bring to an organization through the responsible and skilled use of their individual networks. Employers should enable and train all employees to skillfully utilize social media, pay for learning opportunities and institute a formal system of knowledge transfer whenever external learning occurs, and even start a “networking fund” and allow employees to expense networking lunches.
Corporate Alumni Networks
The third strategy in The Alliance is that organizations should network with ex-employees substantially more than most currently do, specifically by creating corporate alumni networks to facilitate lifelong alliances between organizations and former employees. The authors note extensive potential ROI from corporate alumni networks, including the ability to hire more great people through referrals, new customers, access to competitive and network intelligence, and alumni as brand ambassadors. The authors provide specific how-to guidance on setting up and running corporate alumni networks, ranging from the relatively low-cost to the highly-involved.
Overall, The Alliance: Managing Talent in the Networked Age (find in a library) turns some existing talent management practices sideways, if not upside down. While the authors are perhaps too light on caveating that the Silicon Valley talent ecosystem in which they operate may not generalize to other industries or fields, the talent strategies Hoffman, Casnocha, and Yeh are suggesting are by no means reserved for the tech world. The Alliance challenges leaders, managers, and HR strategists to think differently about legacy talent management practices that may no longer fit today’s environment.
Download the first chapter from the book website.
Source for the SlideShare at the top of this post: The Alliance: A Visual Summary from Reid Hoffman
The Personnel Testing Council of Metropolitan Washington (PTCMW) is a Washington DC membership organization for practitioners of industrial-organizational psychology and organization science.
The January 2017 speaker was the outgoing PTCMW president, Matt Fleisher, who heads Global Talent Analytics at FTI Consulting, a global business advisory firm with approximately 5,000 employees and annual turnover of about 1,000 consultants. FTI fields an annual employee engagement survey with a response rate typically between 75 and 85 percent of the workforce.
Matt shared lessons he learned standing up the Talent Analytics function at FTI, many of which echo what I’ve heard from other practitioners in both private industry and government.
Here’s what. So what? Now what…
In the summer of 2016, I had the good fortune to be selected along with nine other students to attend the 2016 Graduate Workshop in Computational Social Science and Modeling (GWCSS) at the Santa Fe Institute in Santa Fe, NM. Carnegie Mellon Professor John H. Miller and University of Michigan Professor Scott E. Page were our excellent Santa Fe faculty guides for the workshop, which included a combination of lectures on complexity science, complex systems, and computational modeling by Professors Miller and Page, as well as other Santa Fe faculty including Chris Kempes, Simon DeDeo, and Mirta Galesic.
The workshop was a two-week, residential intensive in beautiful Santa Fe. Concurrently, SFI runs a four-week Complex Systems Summer School which brings additional notables in the field of complexity and computational social science, including economist Brian Arthur, Robert Boyd whose 1988 book on cultural evolution coauthored by Peter J. Richerson has been cited more than 7000 times, and Doyne Farmer. As a GWCSS attendee, I was able to attend any of the Summer School lectures I could slip away for.
Lectures and discussions were wide-ranging but all anchored in the science of complexity and computational modeling as tools of scientific investigation. A sampling of our discussions in the GWCSS:
* model diversity (Scott Page)
* evolutionary computation (John Miller)
* information theory and conflict (Simon DeDeo)
* network structure and performance (Mirta Galesic)
I was incredibly lucky to attend the 2016 workshop with a distinguished cohort of fellow graduate students, from whom I learned a great deal during conversation, discussion, and spirited debate over the course of our two weeks together.
During the 2016 GWCSS, I began work on an agent-based model to investigate the role of personality variables in situations in a work setting (view project on ResearchGate). This work continues.
I am grateful to have had the opportunity to attend the 2016 Graduate Workshop in Computational Social Science and recommend it highly.
If you have an interest in complexity science and computational social science, consider applying for either the 2017 GWCSS or the 2017 Complex Systems Summer School at the Santa Fe Institute.
Please note that the application deadline for the 2017 GWCSS is 14 February 2017.
More information about the 2016 workshop I attended, including a reading list, is available from the 2016 website and wiki.
At the 2016 Winter Simulation Conference, I presented “Active Shooter: An Agent-Based Model of Unarmed Resistance” (PDF) (view/follow project on ResearchGate), coauthored with William G. Kennedy. The paper appeared in WSC 2016’s Social and Behavioral Simulation track, chaired by Ugo Merlone of the University of Torino and Stephen Davies from the University of Mary Washington.
Presentation:
Background and motivation:
I initially undertook this modeling project during a 2015 seminar on agent-based modeling for military applications (ABM) offered by Kenneth Comer at George Mason University. The issue of active and mass shooters is obviously not unique to the military, but the shootings at Ft. Hood and the very few ABMs [1 , 2] I could find on mass shootings suggest more attention from computational modelers is needed.
Examining mass shootings through the lens of complexity science and agent-based modeling raised an additional wrinkle that’s seldom addressed in the literature or training for active shooter events. Specifically, the prescription to “Run, Hide, Fight” (or “Avoid, Deny Defend”) might work for individuals, but what about the impact one individual’s behavior has on another individual’s likelihood of surviving an active shooter incident? To illustrate, imagine an active shooting is unfolding and a potential victim is able to run and hide, barricading himself in a supply closet. What happens when the next potential victim arrives at that same supply closet, having also planned to use it? The individual inside the closet has monopolized the secure hiding space, leaving the second individual at greater risk.
The study of mass shootings is complicated. Despite disproportionate media coverage, mass shootings are extremely rare and each mass shooting is unique and difficult to generalize. Even the definition of “mass shooting” is debated. In my work, I’ve relied on the U.S. Federal Bureau of Investigation for official definitions, not because I think they are necessarily the best possible, but because they provide an authoritative baseline to study the issue. The FBI considered a “mass shooting” a single incident in which four or more individuals are killed (this is the definition of “mass murder”) involving the criminal use of a firearm. Importantly, this definition means that an incident in which three are shot and killed would not rise to the level of “mass shooting,” nor would an incident in which many were shot but less than four died of their wounds.
Compounding the rarity of mass shootings available for study is the fact that many of the primary sources (including the shooter himself) are often deceased. This fact, combined with methodological issues of relying on eyewitness accounts that are subject to recall errors and bias, makes the study of historical mass shootings difficult. Conducting experiments in which participants believed they could possibly die would be unethical and dangerous. Computational modeling and simulation, on the other hand, would offer an infinitely repeatable virtual laboratory in which to study this issue and would have no possibility of harming human subjects.
I had initially imagined a model that would include a variety of agents: shooter(s), victims, and peacekeepers/LEOs. While developing the model, I realized that what was most important was creating a model that allowed users–those with more subject-matter expertise than me–to calibrate the model to their knowledge. The eventual version 1 was a generalized outdoor model in which a shooter targets and fires upon victims, victims flee the shooter, and, if set by the user, and small proportion of victims attempt to subdue the shooter, as occurred during the thwarted attack in 2015 on a Thalys train traveling from Amsterdam to Paris.
User-settable parameters permit the user to control the shooter’s accuracy, armament, and, importantly, the probability of a “fighter” overcoming a shooter.
Findings:
Initial findings suggest that even with a very small probability of success – a 1 out of 100 chance for each second of the struggle – a very small proportion of “fighters” in a population can potentially subdue an active shooter, even if that shooter has a 50 percent probability per second of disabling the “fighters.”
Model availability:
My model is available under the Apache 2.0 license from OpenABM for others to use in their own research. I encourage other modelers to extend this model and have made several suggestions for doing so in the paper. In particular, I think the idea of rapid collective action–a coordinated group or swarm attack–should be studied as a potential counter to an active shooter.
Abstract:
Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provided to increase chances of surviving active shooter scenarios, usually emphasizing “Run, Hide, Fight.” Evidence from prior mass shootings suggests that casualties may be limited should the shooter encounter unarmed resistance prior to the arrival of law enforcement officers (LEOs). An agent-based model (ABM) explored the potential for limiting casualties should a small proportion of potential victims swarm a gunman, as occurred on a train from Amsterdam to Paris in 2015. Results suggest that even with a miniscule probability of overcoming a shooter, fighters may save lives but put themselves at increased risk. While not intended to prescribe a course of action, the model suggests the potential for a reduction in casualties in active shooter scenarios.
Keywords: agent-based modeling, ABM, active shooter, mass shooter, military, law enforcement, LEO, firearms, guns, violence, terrorism, security
Full Reference:
Briggs, T. W., & Kennedy, W. G. (2016, December). Active shooter: an agent-based model of unarmed resistance. In Proceedings of the 2016 Winter Simulation Conference (pp. 3521-3531). IEEE Press. https://doi.org/10.1109/WSC.2016.7822381 (PDF)
At the International Congress on Agent Computing, held at George Mason University in Fairfax, VA, November 29-30, 2016, economist Brian Arthur posed six questions for the future of economics.
Arthur described his view that we are now in a time of:
1. Fundamental uncertainty – agents simply don’t know what they face – they must constantly learn, “cognize,” adapt, and grow
2. Technologies that keep changing – all times, all levels: there is permanent disruption. Whatever the tech game is now, it will be different in just 2 years.
We’re now in a world where forecasts, strategies, and actions are being “tested” for survival within situations – an ecology – that those forecasts, strategies, and actions created.
This is the essence of agent-based modeling (ABM).
Six questions:
1. What would a non-equilibrium economics look like?
2. What temporary phenomena would we see?
3. What do complexity economics and political economy have to say to each other?
4. How does the economy form over time and how does it change in structure?
5. What insights would non-equilibrium economics have for policy?
6. Can we have a third rigorous branch of economics – computational economics?
At the 2016 conference of the Computational Social Science Society of the Americas (CSSSA), I presented a paper, coauthored with Andrew Crooks, titled “Close, But Not Close Enough: A Spatial Agent-Based Model of Manager-Subordinate Proximity.” In the paper we present our preliminary effort to explore how workplace layout impacts manager-subordinate interaction likelihood. We developed a spatial agent-based model to simulate how the physical seat locations of individuals with reporting relationships might enhance or detract from an effective manager-subordinate relationship.
Initial findings from the model suggest that when subordinates are separated from their manager by even a single floor in a building, they are substantially less likely to encounter their manager during a typical day or week. This results in same-floor or same-space subordinates receiving a disproportionate share of “unearned” managerial attention than different-floor colleagues, with many potential implications for issues like onboarding, feedback and performance management, and potentially career advancement.
Below you can read the abstract of our paper and find more information about the model.
Abstract:
Employees may be co-located with their manager or they may be separated by distances ranging from a short walk to across oceans, with many gradations in between. Some distances, such as those between floors of an office building, are physically short but may be psychologically quite far. The current project developed a spatial ABM to examine the likelihood of unplanned manager-subordinate encounters in an office setting with two floors. Early results suggest that subordinates located on a different floor than their manager are substantially less likely to have even a single spontaneous encounter with their manager in a work day, despite a relatively short physical separation. If leader-follower (i.e., manager-subordinate) relationships are influenced by spontaneous face-to-face encounters, this finding represents a challenge for organizations with managers having subordinates who are close, but not close enough. Additionally, attempting to impose top-down requirements to travel between floors (e.g., when scheduling meetings) may do surprisingly little to abate this problem. Implications of these findings for organizations are discussed, as are limitations and future research, including possibilities for future model verification and validation.
Keywords: workplace design, supervision, leadership, management, employee performance, virtual teams, leader distance, collaboration, agent-based modeling, ABM
Full Reference:
Briggs, T.W. and Crooks, A.T. (2016), Close, But Not Close Enough: A Spatial Agent-Based Model of Manager-Subordinate Proximity. The Computational Social Science Society of Americas Conference, Santa Fe, NM. (View project on ResearchGate).
I stumbled on Charles Kadushin’s excellent book Understanding Social Networks: Theories, Concepts, and Findings (find in a library) last year while preparing for my PhD qualifying exams. I already own Wasserman and Faust’s Social Network Analysis: Methods and Applications, which is pretty much the go-to text and reference on SNA, as well as Borgatti, Everett, and Johnson’s Analyzing Social Networks but as a social scientist, I was looking for social science applications of network science, and Kadushin’s highly accessible book fit the bill nicely.
Kadushin, emeritus Professor of Sociology at the CUNY Graduate Center, has been engaged in social science research on network topics since the mid 1960s and has example after example of not only his own work with networks in social science, but also citations of all of the other social scientists I’d expect to see: Ron Burt, Ed Laumann, Stanley Milgram, Stephen Borgatti, Daniel Brass, and Barry Wellman, to name only a few.
Kadushin takes a decided and purposefully social approach to social networks, noting in his introduction that although network science can be applied to power grids, for example, understanding social networks really requires examining them “as if people mattered.” Kadushin proceeds to explore both the psychological and sociological theories underpinning networks as well as the social consequences of networks and their structures.
The first few chapters provide an overview of network concepts, moving from individual network members (Chapter 2) through entire social networks and their subcomponents and network properties (Chapter 3) and finally network segmentation (Chapter 4).
Chapter 5 explores the psychological foundations of social networks and the book continues through successive levels, next examining small groups and leaders (Chapter 6), then entire organizations (Chapter 7), small-world networks and community structures (Chapter 8), followed by network processes like influence and diffusion (Chapter 9). Chapter 10 explores social capital as a function of networks and network position and Chapter 11 gives much-needed attention to ethical dilemmas in social network research. Finally, Chapter 12 reviews “ten master ideas” of social networks.
I found Kadushin’s book extremely helpful in pointing to citations of social network analysis applied to social science. For any social scientist interested in social networks, I’d strongly recommend starting with Understanding Social Networks (with Borgatti, Everett, and Johnson’s Analyzing Social Networks as a second choice). I will also note that while Kadushin focuses on social science, he does not shy away from covering the work of physicists and others on networks, though he avoids mathematics in his explanations (but references the appropriate papers).
Likewise, for the general reader, I can’t think of a better book that explains social networks and their applications to social science and social ideas than what Kadushin offers here. An additional strength of the book is Kadushin’s enjoyable writing style and clear and concise recap at the end of each chapter in which he informs the reader “where we are now.”
My physical copy of Understanding Social Networks: Theories, Concepts, and Findings is heavily annotated so I also ended up buying the Kindle version, which was only $9.99 at the time of this writing. (The paperback version is $19.96 on Amazon at the time of this writing, but Amazon’s prices do regularly fluctuate).
In sum, Kadushin’s Understanding Social Networks: Theories, Concepts, and Findings (find in a library) is probably the most enjoyable book on social networks I’ve read and has been particularly helpful in identifying particular applications of network science in the social sciences.
A colleague recently asked how to get started with agent-based modeling (ABM).
It’s never been easier to learn ABM, whether you’re a social scientist, physical scientist, engineer, computer scientist, or from any discipline, really.
If you want to start right this minute, the very best thing to do is to head over to Uri Wilensky’s NetLogo website, download NetLogo (available for any OS) free of charge, and then work through the three learning tutorials available under “Learning NetLogo” in the User Manual.
The first tutorial is titled “Models” and, as its title suggests, introduces you to interacting with existing NetLogo models such as the Wolf-Sheep Predation model of an ecosystem.
The second tutorial is titled “Commands” and takes you a bit deeper in issuing commands to the NetLogo interface.
The third tutorial is titled “Procedures” and walks you through building a model from scratch – writing the necessary NetLogo code to implement a basic agent-based model.
After the three tutorials, the NetLogo website encourages reading through the guides available in the NetLogo documentation (Interface, Info Tab, Programming) and making use of the NetLogo Dictionary, a comprehensive index of NetLogo methods, procedures, and keywords.
What’s great about NetLogo is that it is fairly intuitive and “programming” or “coding” in NetLogo is very quickly learned, making a first agent-based model possible in a very short time.
If you prefer using a textbook as a guide, my recommendation is Uri Wilensky and Bill Rand’s Introduction to Agent-Based Modeling (find in a library), which uses NetLogo and includes companion code and models to run through all of the essentials of agent-based modeling.
Please see my review of Wilensky and Rand’s Introduction to Agent-Based Modeling for more detail on the book – which is excellent – and what it covers.
If you want to get started with ABM, download NetLogo today.
A short 2009 video describing NetLogo and some capabilities:
Uri Wilensky and William Rand’s An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo (find in a library) is the single best book I’ve encountered for anyone interested in agent-based modeling (ABM) in any discipline and at any level (K-12, undergraduate, graduate, professional).
At nearly 500 full-color pages, Wilensky and Rand’s book does an excellent job progressively walking through the decision to use agent-based modeling, creating simple ABMs, extending preexisting ABMs, creating more complicated ABMs, analyzing ABMs, and conducting verification, validation, and replication.
One of the greatest strengths of Wilensky and Rand’s approach is that IABM (Introduction to Agent-Based Modeling) is that it is a hands-on, exploratory book intended for use with the NetLogo multi-agent modeling environment, which is freely available for download. Each chapter of IABM includes many illustrative examples, all implemented and executed in NetLogo. Moreover, the example models and code are not just available to readers (again, free of charge), but are conveniently bundled in the current release of NetLogo. In other words, rather than just read about the models, the reader is encouraged to run the models his or herself. The Chinese proverb says it best:
Tell me and I’ll forget;
show me and I may remember;
involve me and I’ll understand.
Beyond just running the models described in the book, each chapter concludes with a substantial number of exercises or “Explorations,” usually numbering 20 to 30. Each Exploration is a potentially deep opportunity to learn more about ABM by getting involved rather than just reading, as the Chinese proverb suggests.
Wilensky and Rand do a very nice job of using illustrative models from a variety of disciplines; one example might come from the social sciences and the next example from ecology. This is helpful since each reader may come from a different background or have different experience or interests.
The book requires no special background in mathematics or computer science, which is a huge plus in terms of accessibility to a broader audience.
The authors suggest that it could be used as a textbook for an undergraduate course on complex systems or a computer science course on ABM, or even as a supplement to science, social science, or engineering classes. Graduate students who wish to use ABM in their research – regardless of discipline – would likely find IABM one of the best possible places to start. Even experienced researchers with no agent-based modeling experience would benefit from IABM as an introduction to the method.
While the book is aimed at high-level undergraduates and graduate students, it is sufficient to successfully create very detailed and scientifically valuable agent-based models in NetLogo. The authors reserve a final chapter for “advanced” applications potentially of greater interest to individuals interested in specific sorts of ABM: computationally intensive models, participatory or stakeholder-driven modeling, robotics, spatial and geographic information systems (GIS), and network science / social network analysis. They select just a handful of NetLogo’s more advanced capabilities to describe in this chapter, but include helpful references enabling interested readers to learn more.
I can’t find anything about IABM to criticize, though reading it cover to cover (as I did) is certainly an investment of time, albeit a worthwhile one for the reader wishing to learn and use agent-based modeling.
One of the best ways to explore the science of complexity and how complexity theory can be applied to the numerous real-world phenomena we experience and study is through agent-based modeling. Uri Wilensky and Bill Rand have written an excellent book to help anyone do just that, and I recommend An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo (find in a library) to anyone wishing to get started with agent-based modeling.
Wilensky and Rand make ABM accessible and, importantly, thoroughly enjoyable to learn.
The book’s companion website is http://www.intro-to-abm.com/.