Linked lives: the utility of an agent-based approach to modelling partnership and household formation in the context of social care

The UK’s population is aging, which presents a challenge as older people are the primary users of health and social care services. We present an agent-based model of the basic demographic processes that impinge on the supply of, and demand for, social care: namely mortality, fertility, health-status transitions, internal migration, and the formation and dissolution of partnerships and households. Agent-based modeling is used to capture the idea of “linked lives” and thus to represent hypotheses that are impossible to express in alternative formalisms. Simulation runs suggest that the per-taxpayer cost of state-funded social care could double over the next forty years. A key benefit of the approach is that we can treat the average cost of state-funded care as an outcome variable, and examine the projected effect of different sets of assumptions about the relevant social processes

Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation

Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Risk, Institutions and Growth: Why England and Not China?

We analyze the role of risk-sharing institutions in transitions to modern economies. Transitions requires individual-level risk-taking in pursuing productivity-enhancing activities including using and developing new knowledge. Individual-level, idiosyncratic risk implies that distinct risk-sharing institutions – even those providing the same level of insurance – can lead to different growth trajectories if they differently motivate risk-taking. Historically, risk sharing institutions were selected based on their cultural and institutional compatibility and not their unforeseen growth implications. We simulate our growth model incorporating England’s and China’s distinct pre-modern risk-sharing institutions. The model predicts a transition in England and not China even with equal levels of risk sharing. Under the clan-based Chinese institution, the relatively risk-averse elders had more control over technological choices implying lower risk-taking. Focusing on non-market institutions expands on previous growth-theoretic models to highlight that transitions can transpire even in the absence of exogenous productivity shocks or time-dependent state variables. Recognizing the role of non-market institutions in the growth process bridges the view that transitions are due to luck and the view that transitions are inevitable. Transitions transpire when ‘luck’ creates the conditions under which economic agents find it beneficial to make the choices leading to positive rates of technological change. Luck came in the form of historical processes leading to risk-sharing institutions whose unintended consequences encouraged productivity-enhancing risk-taking.institutions, risk, growth, development

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Demand and Capacity Modelling for Acute Services using Discrete Event Simulation

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Health Systems following peer review. The final publication [Demir, E., Gunal, M & Southern, D., Health Syst (2016), first published online March 11, 2016, is available at Springer via http://dx.doi.org/doi:10.1057/hs.2016.1 © 2016 Operational Research Society Ltd 2016Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages) a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5 year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in A&E, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, e.g., future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations, and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for 1,000’s of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions which are supported by statistically validated data.Peer reviewedFinal Accepted Versio

On The Rise of Health Spending and Longevity

We use a calibrated stochastic life-cycle model of endogenous health spending, asset accumulation and retirement to investigate the causes behind the increase in health spending and life expectancy over the period 1965-2005. We estimate that technological change along with the increase in the generosity of health insurance may explain independently 53% of the rise in health spending (insurance 29% and technology 24%) while income less than 10%. By simultaneously occurring over this period, these changes may have lead to a "synergy" or interaction effect which helps explain an additional 37% increase in health spending. We estimate that technological change, taking the form of increased productivity at an annual rate of 1.8%, explains 59% of the rise in life expectancy at age 50 over this period while insurance and income explain less than 10%.demand for health, health spending, insurance, technological change, longevity

How Resilient Are Our Societies? Analyses, Models, and Preliminary Results

Traditional social organizations such as those for the management of healthcare and civil defence are the result of designs and realizations that matched well with an operational context considerably different from the one we are experiencing today: A simpler world, characterized by a greater amount of resources to match less users producing lower peaks of requests. The new context reveals all the fragility of our societies: unmanageability is just around the corner unless we do not complement the "old recipes" with smarter forms of social organization. Here we analyze this problem and propose a refinement to our fractal social organizations as a model for resilient cyber-physical societies. Evidence to our claims is provided by simulating our model in terms of multi-agent systems.Comment: Paper submitted for publication in the Proc. of SERENE 2015 (http://serene.disim.univaq.it/2015/