Two volcanologists from the University of Hawaii at Mnoas Department of Geology and Geophysics have received two of the top three awards from the International Association of Volcanology and Chemistry of the Earths Interior (IAVCEI). Bruce Houghton, the Gordon A. MacDonald Professor of Volcanology and science director of the National Disaster Preparedness Training Center at UH Mnoa was honored recently with the IAVCEI Thorarinsson Medal. Sbastien Biass, a post-doctoral researcher, was honored George Walker Medal.

Bruce Houghton near Ruapehu, New Zealand.

The Thorarinsson Medal is awarded only once every four years by IAVCEI for outstanding contributions to volcanology, and is the highest award in international volcanology.

A giant of volcanology, Bruce has tackled big problems in geology with innovative approaches and technologies, and is truly a scientist of outstanding distinction, stated University of Tasmanias Rebecca Carey in her nomination letter. His research has not only generated a wealth of new scientific understanding, but also critically Thorarinsson-type pioneering advances in long-standing cornerstone volcanologic concepts.

Further, Houghton has pioneered research across the interface of fundamental volcanological science and hazards, social and behavioral science, leading to a world-first detailed training course for scientists, first responders and emergency managers, titled the U.S. FEMA Volcanic Crisis Awareness course.

Houghton and his predecessor at UH Mnoa, George Walker, are among the only nine volcanologists to-date given the Thorarinsson award, an award named for the noted Icelandic geologist and volcanologist Sigurdur Thorarinsson.

Houghton reflected on becoming a Thorarinsson Medalist; I was delighted and surprised by the award. All my research is collaborative and, since moving to UH 70 percent of my papers have been first-authored by my students or postdocs, and these are not the type of statistics that usually lead to such awards. I was particularly pleased because allthree of my mentors in volcanology are in the list of eight prior winners of the medal; it is quite humbling to be joining them. For UH to have been awarded two of the nine Thorarinsson Medal to-date is, I think, a sign that volcanology is in excellent health here in Hawaii. The challenge now is to find ways to build on this reputation and capture for UH some of the wonderful crop of young volcanologists on the market.

Sbastien Biass

The George Walker Awardis given every two years to a young scientist up to seven years after acquiring a doctoral degree. The award recognizes achievements of a recent outstanding graduate in the fields of research encompassed by IAVCEI.

Biass, post-doctoral researcher working with Houghton at UH Mnoa School of Ocean and Earth Science and Technology, was honored for achievements that are all deeply rooted in field studies and because of his unique appreciation with the importance of statistical and critical treatment of field data within the growing field of numerical modelling, cited professor Costanza Bonadonna of the University of Geneva. His unique approach, stems from combining thorough field studies with state-of-the-art numerical modeling, furthering both deposit characterization and the newly-born discipline of hazard and risk assessment that he is pioneering. What makes Sbastien unique in his science is his open mind and multidisciplinary approach, his scientific curiosity and enthusiasm and his dedication to going beyond his own limits.

Biass commented, My vision of the IAVCEI George Walker Award for early career scientist is closely tied to my vision of scientific research, which contains three components. First, scientific curiosity is one of the greatest source of pleasure in life and provides the motivation to attempt understanding the unknown. Second, luck, in the selection of work colleagues, has been an integral part of my research. Specifically, Costanza Bonadonna and Bruce Houghton, both part of the UH family in either past or present, have shown me how working on interesting science with bright people is an invaluable source of satisfaction. Thirdly, I see research as having a global objective of the wellbeing of society, which in volcanology translates to a better understanding of the physics of hazardous processes occurring during eruptions in order to mitigate better the impacts on exposed communities. This award therefore represents a success on these three levels and belongs as much to everyone I have ever looked up to as it does to me. Having been picked amongst a long list of such successful young scientists humbles me and gives great motivation to pursue my scientific career.

The award honors the memory of former UH Mnoa geology professor George Walker, whose discoveries pioneered a modern quantitative approach to physical volcanology and greatly accelerated understanding of volcanic processes.

Originally posted here:
Mnoa volcanologists receive top international awards - UH System Current News

DALLAS They are young and committed to the technology they've spent years and their own money developing. But unlike some other fintech entrepreneurs, they have no ambition to upend wealth management.

Assembled at the annual conference for the XY Planning Network, some of the finalists vying for top prize in its fintech competition are in fact RIAs themselves.

Mark Friedenthal is both president of an eponymously-named RIA firm in New Jersey and CEO of Tolerisk, a startup he launched to develop better risk assessment software for advisors.

Jantz Hoffman runs his Washington-state based RIA, a non-profit and a startup called CSLA Tech, developing a tool for advisors to better help clients struggling with student debt.

They balance the multiple roles, driven by common beliefs that advisors need better technology to stay competitive and that they can help them better than anyone else.

"The top of the class understands they need to embrace technology," Friedenthal says. "It's very difficult to provide competitive advice without technology. But it is easier and becoming more intuitive to use for advisors and clients."

Hoffman agrees, adding that advisors will need new tech tools to address the needs of the next generation of wealth management clients. "We want to teach others what I've learned," he says. "We want to be able to provide them with a tool to do the work."

Friedenthal, who says one of the key innovations in his software is its ability to perform two levels of risk tolerance, sees all advisors working for the best interests of clients.

"We've made a big bet that the future of advice and planning is entrenched in the fiduciary standard and culture," he says. "If you believe that's the future, everybody is going to need technology to do that."

Motivating Hoffman is his idea that RIAs are best situated to help young investors burdened by large amounts of student debt, a client that the industry has traditionally shunned, he says.

"It's short-sighted, as everyone now has student loans," he says. "The old guard doesn't see the big picture. The assets will come. I have nurses, teachers, doctors, lawyers, all have tons of debt. But if I can help them minimize that, I'll have built those relationships, and that will help me build assets."

LABOR OF LOVEThe effort to bring better behavioral science to advisors is partly a personal mission for Sarah Stanley Fallaw, whose platform DataPoints is based on the work done by her father, Thomas J. Stanley, which he used to write his best-selling book The Millionaire Next Door.

"It's a labor of love, it is something he would've been proud of," Fallaw says. "He would want to get this in the hands of people to improve the way they live their lives."

Fallaw sees advisors opening up to the potential of behavioral finance tools and data as they look for ways to provide more insightful advice.

"They are slowly changing to where they value science," she says. "You can pinpoint behaviors that an advisor can use to help coach clients. Talking about spending is not as exciting as investment returns, but it gets at the heart of how well an individual is managing their life."

Advisors can better manage their practices too, says Elsa Chan, senior vice president of business development at Vestwell, a digital retirement platform.

Coming to wealth management, Chan realized how much advisors have to take on and why they need better tools.

"They are all entrepreneurs themselves, all working for commissions," Chan says. "I was amazed at the amount of work that they have to do just to set up and build the business. Helping them grow their business is very rewarding."

She hopes that the advisor tech industry can adopt the collaborative tone she found among her competitors, particularly around integration.

"There are different options out there for advisors, but a lot of tools are siloed," she says. "Advisors do not want a login for every tool; they want a single dashboard to their clients and to be able to efficiently manage those assets through one platform."

Suleman Din is managing editor of SourceMedia's Investment Advisor Group. Follow him on Twitter at @sulemandn

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Developing technology with advisors at heart - Financial Planning

Imagine you are trying to get children to do household chores for the summer. You decide to offer them an incentive of $5 per completed chore. At the beginning of summer, this works great; work is getting done and the kids enjoy the reward. However, after a few weeks, the system has fallen apart. The children only do the chores they enjoy, they neglect other jobs, and the quality of their work has decreased. Should you change the reward amount? Should different chores have different incentives? Should each child receive different rewards? Did this system work better last year? What do your neighbors do to get their kids to do chores?

In this simple example of human behavior, there is a broken incentive system and a lack of data to help determine how things should change. This scenario is not a far comparison to many organizations sales compensation systems. Every year, new compensation plans roll out to the sales teams who go out and sell based on how they believe they will be compensated. As of 2017, 90 percent of U.S. companies change their compensation plans on an annual basis. But are these adjustments optimal are they going to lead to desired business outcomes? Is there a better way to identify which changes need to be made, and when to make them?

Traditionally, sales compensation changes are based more on intuition than data, but often there is some level of analysis that goes into designing and adjusting incentives. However, most reports today are all hindsight and adjustments are made well after there is any potential to change behavior before the end of the quarter or end of the year. What if optimal changes could be identified mid-period and put in place well before it is too late? Better yet, what if the incentive compensation solution itself could identify, recommend and implement plan changes? The most practical way to do this is with machine learning.

The concept of machine learning is not exactly new in the world of technology. It has been around in a variety of forms for decades, but its application to enterprise software is relatively new, particularly with sales incentive management, and its popularity is growing. There are many factors making machine learning realistically applicable in the business world changes in the economics of cloud computing (cheaper than ever before), cloud storage, proliferation of sensors driving Internet of Things (IoT), pervasive use of mobile devices that consume gigabytes of data in minutes, and freely available algorithms are all major contributors to accelerating machine learning adoption. Add to these the complex problems companies face including managing sales compensation, and the perfect environment is in place for machine learning to dramatically proliferate.

Machine learning is all about applying learned data to prescribe more economically efficient business decisions. Sales incentives apply the disciplines of psychology and behavioral economics to prompt people to make desired decisions. When you combine principles of behavioral economics with the data science of machine learning, you create the potential to optimize your sales incentives and drive powerful business outcomes.

Machine learning allows us to assess large sets of data and surface patterns, identifying when past performance is indicative of future results. For instance, machine learning can accurately forecast what products are most likely to be sold and which customers are most likely to buy. But what if you not only want to understand potential outcomes, what if you want to completely change outcomes?

What is going to motivate your sales team to do what you need them to? The difference between expectations and reality is often referred to as the behavioral gap (see chart below). When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive (an added bonus, Presidents Club eligibility, a promotion, etc.) can initiate an inflection point and influence a change in behavior.

The behavior gap depicted above represents the difference between raised expectations (management increasing quota) and the trajectory of current sales performance.

In the US, studies from Harvard Business Review and other industry publications estimate that companies spend over one trillion dollars annually on incentives. That number is four times the money spent on advertising in the US annually. What that means is that, as a nation, we are deeply invested in motivating our employees, partners and customers. Incentives are most effective when they are intelligent, or data driven. Deloitte University Press published a report stating that when it comes to the relationship between data science and behavioral science, it is reasonable to anticipate better results when the two approaches are treated as complementary and applied in tandem. Behavioral science principles should be part of the data scientists toolkit, and vice versa.

With Machine learning and behavior mechanics, sales teams can plot out the path from one goal to the next and analyze and implement proper incentives. As an example, lets say your company is a furniture manufacturer that uses a CPQ tool to manage its complex quoting and pricing processes. One of the major reasons your company invested in the CPQ solution was to curb chronic, costly discounting by the sales team. You are a new sales rep using CPQ to build a quote. What if, mid-quote, your system alerts you that the discount you entered, while within the approved range, may not be ideal. Machine learning ran in the background and identified a different discount used by the top 10% of reps that has had more success. Additionally, you learn that if you choose the prescribed discount, you will earn 40% more commission! Talk about a relevant incentive, based on powerful data.

When applied together, machine learning and sales incentives provide powerful business results by collecting relevant, timely insight and defining incentives that align human behaviors with organizational goals.

Original post:
Sales Incentives and Machine Learning: Intelligently Motivate Revenue-Driving Behaviors - Customer Think