Toward a computational immigration assistant
First Monday

Toward a computational immigration assistant by Bill Tomlinson



Abstract
In the face of global change and impending limits on various resources, the need for people to move across borders is likely to increase. Facilitating mobility could enable people to access resources more effectively, and thereby reduce suffering for both humans and other species. This paper proposes the need for computational support to facilitate immigration decisions across a range of scales, from the individual to the community to the national government. Drawing inspiration from global systems modeling, social networking, and collaborative filtering, these computational tools would help match up individuals and communities seeking to emigrate with potential host countries, based on the preferences of both.

Contents

1. Introduction
2. Individual/community tools
3. Governmental tools
4. Matchmaking
5. Concerns
6. Conclusion

 


 

1. Introduction

A decision to move to a new country is rarely undertaken lightly. Individuals may desire a better life for their children. Communities may be fleeing persecution, starvation, or displacement in their home country. People decide where to immigrate based on geographical proximity, family connections, stories they’ve heard, employment opportunities that arise, and many other factors. However, these factors do not necessarily lead to effective decisions. There are few tools that effectively support these decision processes.

From the other side, national governments often have preferences with regard to which kinds of individuals and communities, if any, they would like to have immigrate into their countries. These preferences may not always be just, or favored by citizens of other countries, or even favored by many citizens of the country itself; nevertheless, national sovereignty typically carries with it the capacity to “control entry” (Weiner, 1996). In addition, countries may have preferences about individuals or groups whom they would prefer to have move out of their country (e.g., people whose behaviors or lifestyles are illegal in one country, but not in another). In the absence of a mechanism for developing global consensus about the most equitable immigration policies, we may nevertheless hope for transparency and efficiency at enacting those policies that do exist.

Certain countries have relatively clear and timely immigration policies for individuals. New Zealand, for example, has a “points” system that determines whether or not a person will be allowed to immigrate (https://www.immigration.govt.nz/pointsindicator/). The U.S. immigration process, on the other hand, is renowned for its byzantine complexity. Still other countries allow very little immigration at all.

Immigration is typically dealt with on an individual or family basis. Inter-country migration of whole communities, rather than specific individuals/families, is rare, and usually resulting from and accompanied by significant suffering (e.g., four million people fleeing the civil war currently under way in Syria). Nevertheless, in the wake of climate change, it is becoming increasingly likely that whole communities might seek to move to new locations. For example, the Republic of Maldives, a country in the Indian Ocean with a highest point of 2.4 meters above sea level, is seeking to purchase land in a different country, so that all of its residents do not become refugees if sea levels continue to rise.

This paper proposes that computational support may be beneficial in the match-making process between individuals/communities and nations to which they might immigrate. The paper seeks to present a potential framework by which such support could come into existence.

In this effort, I do not mean to downplay the complexity of the myriad factors that go into such transitions. An immigration tool will not change the geographic, personal, cultural, and other factors that strongly influence immigration. However, computational support could reduce many of the transaction costs involved in the process, and could prove beneficial both for immigrants and host countries. And computational support could potentially allow for immigration to happen at sufficiently large scales that entire communities could undertake the transition together, thus allowing existing social ties to be preserved despite the need for migration.

In a world where various limits on the availability of resources (e.g., peak oil, peak phosphorus) come into play more powerfully than they do at present, a mismatch between the carrying capacity of a nation and the human population of that nation could lead to profound suffering and population reductions for both humans and other species. For example, in the face of limits to the availability of fossil fuels, Southern California’s carrying capacity could drop drastically as imported water and other resources become scarce. In light of these potential transformations in carrying capacity, it is potentially valuable to have a more streamlined way to address the complex, multi-national problem of immigration.

There have been efforts to use computational modeling to support decision processes with regard to immigration between particular countries, such as between the US and Mexico (Crowe and Lucas-Vergona, 2007). Computational modeling has also been used to understand the migration of various animal species (Fink, et al., 2013). There are also online tools to evaluate various aspects of quality of life in various countries (e.g., http://www.oecdbetterlifeindex.org/). However, I have not been able to find any computational tools to support the immigration process more broadly, across a range of countries, communities and individuals.

There are, though, many online tools in existence to help match people with other people and with institutions that are well suited to them. Facebook.com suggests friends, Match.com suggests romantic partners, and CareerBuilder.com offers potential jobs. Matching people with goods and services is also a flourishing activity online, with Amazon.com, Netflix.com, and many other online retailers suggesting appropriate purchases.

I seek to draw on the social networking and collaborative filtering that is common on the Internet and combine it with a tool for modeling the state of various geographic regions at specific points in the future given various scenarios, similar to the World3 model used in the Club of Rome’s Limits to growth (Meadows, et al., 1972). I propose the need for two interacting suites of tools, one for individuals/community and another for governments.

 

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2. Individual/community tools

The costs of moving one’s whole life — social ties, possessions, livelihoods, engagement with established infrastructures, etc. — make it disadvantageous to do so frequently.

In the very short term, these transaction costs often overshadow any but the most powerful benefits that might be reaped. For some people, though, circumstances such as government persecution or environmental crises have already uprooted livelihoods, families, and communities making mobility obligatory rather than optional.

On a longer time horizon, even for individuals and groups with viable livelihoods and stable homes, the desire to improve circumstances for oneself and one’s descendants may overcome the challenges of uprooting many different aspects of one’s way of living.

Both powerful short-term and pervasive long-term mismatches between one’s present and desired ways of living feature predictable regularities that make them amenable to computational support. If one individual is starving or being oppressed in a particular context, there are frequently many others in a similar situation. And if there is one person in fear for his or her long term future, there are likely others as well. Pervasive social, environmental, political, religious, or other problems may cause entire communities to seek, or be forced into, migration.

There are numerous factors that could influence people’s decision to emigrate from their current country. In the proposed system, I would begin with a global model of the carrying capacity of various regions, inspired by systems such as World3 and http://dashboard.carryingcapacity.com.au/. In addition, the system would allow individuals to input information about themselves (current citizenship, languages spoken, etc.) and use this information to predict the degree to which those individuals could take advantage of local resources. Connections to existing social networking systems (e.g., Facebook’s 1.4B active users), could allow communities to engage in collective decision-making processes about best places to immigrate. The system could also provide links to expat social networks in particular regions, as well as to skill-building and community-building resources relevant to that region. Through such a system, an individual or group could make a more informed decision about how well suited a given region is for providing their needs across particular time horizons.

From the point of view of the individual, this set of tools is an instance of a “self-obviating system” (Tomlinson, et al., 2015) — “one in which the successful operation of the system in the short term renders it unnecessary in the long term”. The successful deployment of an immigration assistant for individuals or communities would help them find a better home nation and assimilate into that nation’s culture; once it had done so, the individual/community users would gradually lose the need for such a system.

 

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3. Governmental tools

Coupled with the problem of individuals and communities deciding where to immigrate is the problem of governments deciding whom to accept. Immigration policy is hotly contested in governments around the world. Governments may wish to have immigrants with particular skills, particular resources (e.g., money), or particular ages, or that fit many other criteria. Governments at particular time periods may take an active role in recruiting immigrants (e.g., “Give me your tired, your poor, your huddled masses yearning to breathe free, the wretched refuse of your teeming shore ...” engraved on the U.S. Statue of Liberty), or may actively seek to prevent particular instances of immigration (e.g., the border fence between the U.S. and Mexico) or emigration (e.g., the Berlin Wall). In addition, governments may wish to encourage certain individuals or groups to leave a country, and could be willing to compensate another country to accommodate them.

Creating a suite of governmental tools that pair effectively with the individual/community level immigration tools could enable governments to recruit residents that match their needs more effectively. This suite could include ways of specifying particular desirable attributes, and conditions on which the immigration would be predicated (e.g., New Zealand requires people above a certain age to be able to invest particular amounts of money in New Zealand-based assets for specific lengths of time). These conditions could also work the opposite direction, with countries being willing to pay particular individuals or groups to move to their country, or offering other non-monetary incentives.

 

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4. Matchmaking

Connecting the individual/community tools to the governmental tools has the potential to enable both systems to work more effectively. Making mutually beneficial reconfiguration faster could allow for more rapid improvement in quality of life both for the immigrants and for the other citizens of their original and new host countries. The process of matchmaking could draw on computational techniques from collaborative filtering and social networking to offer both individuals and governments suggestions for appropriate matches. Enabling people and governments to learn about non-viable pairings more transparently and more quickly could allow everyone to move on with their lives, rather than living in a state of uncertainty while decades pass as paperwork is processed (as sometimes occurs in the U.S. and elsewhere). Documenting both successful and unsuccessful pairings (e.g., via a Yelp-like review system) could help future individuals and communities benefit from the experiences of those who have gone before, and help governments revise their policies appropriately.

Computational support for global immigration could enable the process to be more like university admissions and hiring, where there is an annual admissions/hiring season, or Match Day in the U.S. medical school process, where individuals are dynamically matched with training programs each year. While these systems are by no means flawless, they nevertheless provide a known, relatively standardized set of procedures, which eases the burdens on all involved. By standardizing the process, a broader market of supporting tools (travel planning, logistical coordination, legal services) could also arise.

 

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5. Concerns

Such a system brings to the surface a number of problems.

Emigration often disrupts social ties. Even if it is advantageous for the individual(s) emigrating, it may be harmful to others who are no longer able to benefit from ongoing interactions with them. And both those emigrating and those remaining in the home country may not be fully aware of the degree to which this disruption will occur. Nevertheless, this problem suggests that such a system should help its users and their social partners take these factors into account, rather than that it should not be built at all.

Second, if a country or other organization (U.N., NGO, etc.) is willing to compensate a different country to accommodate refugees or other individuals for whom their home country is no longer a viable place to live, there must be some mechanism in place to guarantee the fair treatment of those individuals. It may be the case that such a mechanism is impossible to implement, and thus the system is non-viable. There are numerous examples of organizations, such as the U.N., finding it challenging to establish the degree to which human rights violations are occurring in a particular region. However, a computational immigration assistant would at least provide a mechanism for documenting the identities of the migrants, and thus providing a greater level of accountability than with a country’s own citizen.

A third challenge is that many people, including some who might most be in need of the services that a computational immigration assistant might provide, may not have access to computing or other resources to be able to use such a system. Without access, they would be disenfranchised from the process of improving their circumstances. Such a system could easily be biased in favor of individuals who already have access to wealth and power.

A fourth challenge is that the system would require individuals, groups, and nations to make explicit their preferences. Many people have preferences that others, and sometimes they themselves, may find distasteful (e.g., not wishing to live near people of a different race); these people may be reluctant to document these preferences in a computational form. However, I would argue that is not the computational support for immigration that is problematic, but rather the fact that discrimination and oppression and poverty and many other societal ills exist at all. Making these issues more explicit via computational operationalization could help foreground them, and thereby make it more likely that they could be resolved.

 

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6. Conclusion

While it is not realistic to create a perfect system for matching individuals and communities seeking to leave their homelands with countries desiring to host them, there is nevertheless the possibility for a more unified system for immigration. Such a system could allow for more effective pairings between individuals, communities, and nations. In the face of global limits and climate change, enabling more rapid mobility across borders could enable people to restructure their lives more effectively, and prevent avoidable suffering. End of article

 

About the author

Bill Tomlinson is professor in the Department of Informatics at the Donald Bren School of Information and Computer Sciences, University of California, Irvine, and a researcher at the California Institute for Telecommunications and Information Technology. He is the author of Greening through IT: Information technology for environmental sustainability (Cambridge, Mass.: MIT Press, 2010).
E-mail: wmt [at] uci [dot] edu

 

Acknowledgments

I would like to thank the participants in the LIMITS 2015 workshop for their feedback on the ideas in this paper.

 

References

S. Crowe and J. Lucas-Vergona, 2007. “What should be done about the illegal immigration from Mexico to the United States?” Mathematical and Computer Modelling, volume 46, numbers 7–8, pp. 1,115–1,129.
doi: http://dx.doi.org/10.1016/j.mcm.2007.03.026, accessed 23 July 2015.

D. Fink, T. Damoulas, and J. Dave. 2013. “Adaptive spatio-temporal exploratory models: Hemisphere-wide species distributions from massively crowdsourced eBird data,” Twenty-Seventh AAAI Conference on Artificial Intelligence, at https://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6417, accessed 23 July 2015.

D. Meadows, G. Meadows, J. Randers, and W. Behrens, 1972. The limits to growth: A report for the Club of Rome’s project on the predicament of mankind. New York: Universe Books.

B. Tomlinson, J. Norton, E.P.S. Baumer, M. Pufal, and B. Raghavan. 2015. “Self-obviating systems and their application to sustainability,” iConference 2015, at http://www1.icsi.berkeley.edu/~barath/papers/selfobviating-iconference15.pdf, accessed 23 July 2015.

M. Weiner, 1996. “Ethics, national sovereignty and the control of immigration,” International Migration Review, volume 30, number 1, pp. 171–197.
doi: http://dx.doi.org/10.2307/2547466, accessed 23 July 2015.

 


Editorial history

Received 15 July 2015; accepted 23 July 2015.


Creative Commons License
“Toward a computational immigration assistant” by Bill Tomlinson is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Toward a computational immigration assistant
by Bill Tomlinson.
First Monday, Volume 20, Number 8 - 3 August 2015
https://firstmonday.org/ojs/index.php/fm/article/view/6119/4838
doi: http://dx.doi.org/10.5210/fm.v20i8.6119





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